{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit WARNING: [17:12:07] Enabling RDKit 2019.09.3 jupyter extensions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Processing...\n",
      "Loading customized repurposing dataset...\n",
      "Beginning Downloading Configs Files for training from scratch...\n",
      "Downloading finished... Beginning to extract zip file...\n",
      "Configs Models Successfully Downloaded...\n",
      "Training on your own customized data...\n",
      "in total: 26640 drug-target pairs\n",
      "encoding drug...\n",
      "unique drugs: 13763\n",
      "drug encoding finished...\n",
      "encoding protein...\n",
      "unique target sequence: 1\n",
      "protein encoding finished...\n",
      "splitting dataset...\n",
      "Done.\n",
      "Training from scrtach...\n",
      "Begin to train model 0 with drug encoding MPNN and target encoding CNN\n",
      "Let's use 1 GPU!\n",
      "--- Data Preparation ---\n",
      "--- Go for Training ---\n",
      "Training at Epoch 1 iteration 0 with loss 0.69219. Total time 0.00055 hours\n",
      "Training at Epoch 1 iteration 100 with loss 0.67406. Total time 0.01333 hours\n",
      "Validation at Epoch 1 , AUROC: 0.70764 , AUPRC: 0.08025 , F1: 0.09424\n",
      "Training at Epoch 2 iteration 0 with loss 0.62362. Total time 0.02305 hours\n",
      "Training at Epoch 2 iteration 100 with loss 0.52807. Total time 0.03611 hours\n",
      "Validation at Epoch 2 , AUROC: 0.72531 , AUPRC: 0.16038 , F1: 0.10389\n",
      "Training at Epoch 3 iteration 0 with loss 0.47976. Total time 0.04583 hours\n",
      "Training at Epoch 3 iteration 100 with loss 0.46717. Total time 0.05861 hours\n",
      "Validation at Epoch 3 , AUROC: 0.73128 , AUPRC: 0.16780 , F1: 0.13793\n",
      "Training at Epoch 4 iteration 0 with loss 0.47900. Total time 0.06833 hours\n",
      "Training at Epoch 4 iteration 100 with loss 0.40212. Total time 0.08138 hours\n",
      "Validation at Epoch 4 , AUROC: 0.73826 , AUPRC: 0.19690 , F1: 0.12826\n",
      "Training at Epoch 5 iteration 0 with loss 0.40222. Total time 0.09111 hours\n",
      "Training at Epoch 5 iteration 100 with loss 0.23715. Total time 0.10416 hours\n",
      "Validation at Epoch 5 , AUROC: 0.72835 , AUPRC: 0.23509 , F1: 0.23602\n",
      "Training at Epoch 6 iteration 0 with loss 0.27357. Total time 0.11388 hours\n",
      "Training at Epoch 6 iteration 100 with loss 0.35282. Total time 0.12666 hours\n",
      "Validation at Epoch 6 , AUROC: 0.72309 , AUPRC: 0.22652 , F1: 0.21229\n",
      "Training at Epoch 7 iteration 0 with loss 0.08800. Total time 0.13638 hours\n",
      "Training at Epoch 7 iteration 100 with loss 0.21136. Total time 0.14944 hours\n",
      "Validation at Epoch 7 , AUROC: 0.74493 , AUPRC: 0.19183 , F1: 0.25210\n",
      "Training at Epoch 8 iteration 0 with loss 0.17881. Total time 0.15916 hours\n",
      "Training at Epoch 8 iteration 100 with loss 0.11592. Total time 0.17194 hours\n",
      "Validation at Epoch 8 , AUROC: 0.72775 , AUPRC: 0.23099 , F1: 0.25742\n",
      "Training at Epoch 9 iteration 0 with loss 0.11575. Total time 0.18166 hours\n",
      "Training at Epoch 9 iteration 100 with loss 0.14362. Total time 0.19444 hours\n",
      "Validation at Epoch 9 , AUROC: 0.73358 , AUPRC: 0.21052 , F1: 0.23913\n",
      "Training at Epoch 10 iteration 0 with loss 0.17836. Total time 0.20416 hours\n",
      "Training at Epoch 10 iteration 100 with loss 0.08284. Total time 0.21722 hours\n",
      "Validation at Epoch 10 , AUROC: 0.72196 , AUPRC: 0.20493 , F1: 0.25490\n",
      "--- Go for Testing ---\n",
      "Testing AUROC: 0.794694459119799 , AUPRC: 0.29236629647158663 , F1: 0.30630630630630634\n",
      "--- Training Finished ---\n",
      "model training finished, now repurposing\n",
      "repurposing...\n",
      "in total: 82 drug-target pairs\n",
      "encoding drug...\n",
      "unique drugs: 81\n",
      "drug encoding finished...\n",
      "encoding protein...\n",
      "unique target sequence: 1\n",
      "protein encoding finished...\n",
      "Done.\n",
      "predicting...\n",
      "---------------\n",
      "Predictions from model 0 with drug encoding MPNN and target encoding CNN are done...\n",
      "in total: 26640 drug-target pairs\n",
      "encoding drug...\n",
      "unique drugs: 13763\n",
      "drug encoding finished...\n",
      "encoding protein...\n",
      "unique target sequence: 1\n",
      "protein encoding finished...\n",
      "splitting dataset...\n",
      "Done.\n",
      "Training from scrtach...\n",
      "Begin to train model 1 with drug encoding CNN and target encoding CNN\n",
      "Let's use 1 GPU!\n",
      "--- Data Preparation ---\n",
      "--- Go for Training ---\n",
      "Training at Epoch 1 iteration 0 with loss 0.69552. Total time 0.00027 hours\n",
      "Training at Epoch 1 iteration 100 with loss 0.60612. Total time 0.00833 hours\n",
      "Validation at Epoch 1 , AUROC: 0.72360 , AUPRC: 0.23331 , F1: 0.10069\n",
      "Training at Epoch 2 iteration 0 with loss 0.53683. Total time 0.01472 hours\n",
      "Training at Epoch 2 iteration 100 with loss 0.33064. Total time 0.02277 hours\n",
      "Validation at Epoch 2 , AUROC: 0.74836 , AUPRC: 0.22101 , F1: 0.19\n",
      "Training at Epoch 3 iteration 0 with loss 0.23112. Total time 0.02916 hours\n",
      "Training at Epoch 3 iteration 100 with loss 0.03467. Total time 0.0375 hours\n",
      "Validation at Epoch 3 , AUROC: 0.73631 , AUPRC: 0.24006 , F1: 0.25581\n",
      "Training at Epoch 4 iteration 0 with loss 0.09362. Total time 0.04388 hours\n",
      "Training at Epoch 4 iteration 100 with loss 0.02818. Total time 0.05194 hours\n",
      "Validation at Epoch 4 , AUROC: 0.75143 , AUPRC: 0.25542 , F1: 0.29545\n",
      "Training at Epoch 5 iteration 0 with loss 0.03108. Total time 0.05833 hours\n",
      "Training at Epoch 5 iteration 100 with loss 0.00439. Total time 0.06638 hours\n",
      "Validation at Epoch 5 , AUROC: 0.74331 , AUPRC: 0.26049 , F1: 0.19626\n",
      "Training at Epoch 6 iteration 0 with loss 0.09016. Total time 0.07277 hours\n",
      "Training at Epoch 6 iteration 100 with loss 0.04769. Total time 0.08083 hours\n",
      "Validation at Epoch 6 , AUROC: 0.73865 , AUPRC: 0.24654 , F1: 0.19999\n",
      "Training at Epoch 7 iteration 0 with loss 0.00803. Total time 0.08722 hours\n",
      "Training at Epoch 7 iteration 100 with loss 0.11943. Total time 0.09527 hours\n",
      "Validation at Epoch 7 , AUROC: 0.73311 , AUPRC: 0.22922 , F1: 0.26470\n",
      "Training at Epoch 8 iteration 0 with loss 0.01275. Total time 0.10166 hours\n",
      "Training at Epoch 8 iteration 100 with loss 0.01197. Total time 0.10972 hours\n",
      "Validation at Epoch 8 , AUROC: 0.75380 , AUPRC: 0.21910 , F1: 0.25\n",
      "Training at Epoch 9 iteration 0 with loss 0.00258. Total time 0.11611 hours\n",
      "Training at Epoch 9 iteration 100 with loss 0.02608. Total time 0.12416 hours\n",
      "Validation at Epoch 9 , AUROC: 0.74069 , AUPRC: 0.27212 , F1: 0.26262\n",
      "Training at Epoch 10 iteration 0 with loss 0.01071. Total time 0.13055 hours\n",
      "Training at Epoch 10 iteration 100 with loss 0.01437. Total time 0.13861 hours\n",
      "Validation at Epoch 10 , AUROC: 0.73440 , AUPRC: 0.25744 , F1: 0.27906\n",
      "--- Go for Testing ---\n",
      "Testing AUROC: 0.7888349903142661 , AUPRC: 0.32125016124670125 , F1: 0.37777777777777777\n",
      "--- Training Finished ---\n",
      "model training finished, now repurposing\n",
      "repurposing...\n",
      "in total: 82 drug-target pairs\n",
      "encoding drug...\n",
      "unique drugs: 81\n",
      "drug encoding finished...\n",
      "encoding protein...\n",
      "unique target sequence: 1\n",
      "protein encoding finished...\n",
      "Done.\n",
      "predicting...\n",
      "---------------\n",
      "Predictions from model 1 with drug encoding CNN and target encoding CNN are done...\n",
      "in total: 26640 drug-target pairs\n",
      "encoding drug...\n",
      "unique drugs: 13763\n",
      "drug encoding finished...\n",
      "encoding protein...\n",
      "unique target sequence: 1\n",
      "protein encoding finished...\n",
      "splitting dataset...\n",
      "Done.\n",
      "Training from scrtach...\n",
      "Begin to train model 2 with drug encoding Morgan and target encoding CNN\n",
      "Let's use 1 GPU!\n",
      "--- Data Preparation ---\n",
      "--- Go for Training ---\n",
      "Training at Epoch 1 iteration 0 with loss 0.69251. Total time 0.00027 hours\n",
      "Training at Epoch 1 iteration 100 with loss 0.03058. Total time 0.00666 hours\n",
      "Validation at Epoch 1 , AUROC: 0.70575 , AUPRC: 0.21219 , F1: 0.3125\n",
      "Training at Epoch 2 iteration 0 with loss 0.00571. Total time 0.01194 hours\n",
      "Training at Epoch 2 iteration 100 with loss 0.00601. Total time 0.01833 hours\n",
      "Validation at Epoch 2 , AUROC: 0.73703 , AUPRC: 0.27978 , F1: 0.26666\n",
      "Training at Epoch 3 iteration 0 with loss 0.00014. Total time 0.02361 hours\n",
      "Training at Epoch 3 iteration 100 with loss 0.00495. Total time 0.03 hours\n",
      "Validation at Epoch 3 , AUROC: 0.69870 , AUPRC: 0.16020 , F1: 0.21333\n",
      "Training at Epoch 4 iteration 0 with loss 0.12378. Total time 0.03527 hours\n",
      "Training at Epoch 4 iteration 100 with loss 0.00010. Total time 0.04166 hours\n",
      "Validation at Epoch 4 , AUROC: 0.73478 , AUPRC: 0.19567 , F1: 0.2\n",
      "Training at Epoch 5 iteration 0 with loss 0.00012. Total time 0.04666 hours\n",
      "Training at Epoch 5 iteration 100 with loss 0.00428. Total time 0.05333 hours\n",
      "Validation at Epoch 5 , AUROC: 0.71250 , AUPRC: 0.23266 , F1: 0.36585\n",
      "Training at Epoch 6 iteration 0 with loss 0.00166. Total time 0.05861 hours\n",
      "Training at Epoch 6 iteration 100 with loss 0.05938. Total time 0.06527 hours\n",
      "Validation at Epoch 6 , AUROC: 0.72070 , AUPRC: 0.25071 , F1: 0.29508\n",
      "Training at Epoch 7 iteration 0 with loss 8.21252. Total time 0.07027 hours\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training at Epoch 7 iteration 100 with loss 0.00050. Total time 0.07694 hours\n",
      "Validation at Epoch 7 , AUROC: 0.74203 , AUPRC: 0.25694 , F1: 0.32876\n",
      "Training at Epoch 8 iteration 0 with loss 0.00028. Total time 0.08194 hours\n",
      "Training at Epoch 8 iteration 100 with loss 8.92719. Total time 0.08861 hours\n",
      "Validation at Epoch 8 , AUROC: 0.76748 , AUPRC: 0.26690 , F1: 0.30769\n",
      "Training at Epoch 9 iteration 0 with loss 0.00865. Total time 0.09361 hours\n",
      "Training at Epoch 9 iteration 100 with loss 3.01948. Total time 0.1 hours\n",
      "Validation at Epoch 9 , AUROC: 0.75496 , AUPRC: 0.26952 , F1: 0.27118\n",
      "Training at Epoch 10 iteration 0 with loss 4.84512. Total time 0.10527 hours\n",
      "Training at Epoch 10 iteration 100 with loss 1.63913. Total time 0.11166 hours\n",
      "Validation at Epoch 10 , AUROC: 0.73905 , AUPRC: 0.20806 , F1: 0.30769\n",
      "--- Go for Testing ---\n",
      "Testing AUROC: 0.786321502329379 , AUPRC: 0.33377574030682805 , F1: 0.3661971830985916\n",
      "--- Training Finished ---\n",
      "model training finished, now repurposing\n",
      "repurposing...\n",
      "in total: 82 drug-target pairs\n",
      "encoding drug...\n",
      "unique drugs: 81\n",
      "drug encoding finished...\n",
      "encoding protein...\n",
      "unique target sequence: 1\n",
      "protein encoding finished...\n",
      "Done.\n",
      "predicting...\n",
      "---------------\n",
      "Predictions from model 2 with drug encoding Morgan and target encoding CNN are done...\n",
      "in total: 26640 drug-target pairs\n",
      "encoding drug...\n",
      "unique drugs: 13763\n",
      "drug encoding finished...\n",
      "encoding protein...\n",
      "unique target sequence: 1\n",
      "-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n",
      "protein encoding finished...\n",
      "splitting dataset...\n",
      "Done.\n",
      "Training from scrtach...\n",
      "Begin to train model 3 with drug encoding Morgan and target encoding AAC\n",
      "Let's use 1 GPU!\n",
      "--- Data Preparation ---\n",
      "--- Go for Training ---\n",
      "Training at Epoch 1 iteration 0 with loss 0.69258. Total time 0.0 hours\n",
      "Training at Epoch 1 iteration 100 with loss 0.03368. Total time 0.00222 hours\n",
      "Validation at Epoch 1 , AUROC: 0.72777 , AUPRC: 0.23005 , F1: 0.27848\n",
      "Training at Epoch 2 iteration 0 with loss 0.05851. Total time 0.00416 hours\n",
      "Training at Epoch 2 iteration 100 with loss 0.00342. Total time 0.00638 hours\n",
      "Validation at Epoch 2 , AUROC: 0.72116 , AUPRC: 0.19938 , F1: 0.23188\n",
      "Training at Epoch 3 iteration 0 with loss 0.03552. Total time 0.00833 hours\n",
      "Training at Epoch 3 iteration 100 with loss 0.03714. Total time 0.01055 hours\n",
      "Validation at Epoch 3 , AUROC: 0.75044 , AUPRC: 0.24382 , F1: 0.26666\n",
      "Training at Epoch 4 iteration 0 with loss 0.00824. Total time 0.0125 hours\n",
      "Training at Epoch 4 iteration 100 with loss 2.96523. Total time 0.015 hours\n",
      "Validation at Epoch 4 , AUROC: 0.74432 , AUPRC: 0.22218 , F1: 0.22580\n",
      "Training at Epoch 5 iteration 0 with loss 0.00015. Total time 0.01694 hours\n",
      "Training at Epoch 5 iteration 100 with loss 4.28601. Total time 0.01916 hours\n",
      "Validation at Epoch 5 , AUROC: 0.73850 , AUPRC: 0.22437 , F1: 0.27692\n",
      "Training at Epoch 6 iteration 0 with loss 0.00086. Total time 0.02111 hours\n",
      "Training at Epoch 6 iteration 100 with loss 0.00075. Total time 0.02333 hours\n",
      "Validation at Epoch 6 , AUROC: 0.72299 , AUPRC: 0.16720 , F1: 0.11111\n",
      "Training at Epoch 7 iteration 0 with loss 0.00180. Total time 0.02527 hours\n",
      "Training at Epoch 7 iteration 100 with loss 0.01561. Total time 0.0275 hours\n",
      "Validation at Epoch 7 , AUROC: 0.68612 , AUPRC: 0.21516 , F1: 0.28571\n",
      "Training at Epoch 8 iteration 0 with loss 0.00058. Total time 0.02944 hours\n",
      "Training at Epoch 8 iteration 100 with loss 1.33094. Total time 0.03194 hours\n",
      "Validation at Epoch 8 , AUROC: 0.72089 , AUPRC: 0.21749 , F1: 0.27118\n",
      "Training at Epoch 9 iteration 0 with loss 7.78595. Total time 0.03361 hours\n",
      "Training at Epoch 9 iteration 100 with loss 0.01024. Total time 0.03611 hours\n",
      "Validation at Epoch 9 , AUROC: 0.70943 , AUPRC: 0.21453 , F1: 0.24778\n",
      "Training at Epoch 10 iteration 0 with loss 0.00707. Total time 0.03805 hours\n",
      "Training at Epoch 10 iteration 100 with loss 0.07515. Total time 0.04027 hours\n",
      "Validation at Epoch 10 , AUROC: 0.70842 , AUPRC: 0.17618 , F1: 0.22580\n",
      "--- Go for Testing ---\n",
      "Testing AUROC: 0.8021628804252117 , AUPRC: 0.2416151102959611 , F1: 0.2727272727272727\n",
      "--- Training Finished ---\n",
      "model training finished, now repurposing\n",
      "repurposing...\n",
      "in total: 82 drug-target pairs\n",
      "encoding drug...\n",
      "unique drugs: 81\n",
      "drug encoding finished...\n",
      "encoding protein...\n",
      "unique target sequence: 1\n",
      "-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n",
      "protein encoding finished...\n",
      "Done.\n",
      "predicting...\n",
      "---------------\n",
      "Predictions from model 3 with drug encoding Morgan and target encoding AAC are done...\n",
      "in total: 26640 drug-target pairs\n",
      "encoding drug...\n",
      "unique drugs: 13763\n",
      "drug encoding finished...\n",
      "encoding protein...\n",
      "unique target sequence: 1\n",
      "-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n",
      "protein encoding finished...\n",
      "splitting dataset...\n",
      "Done.\n",
      "Training from scrtach...\n",
      "Begin to train model 4 with drug encoding Daylight and target encoding AAC\n",
      "Let's use 1 GPU!\n",
      "--- Data Preparation ---\n",
      "--- Go for Training ---\n",
      "Training at Epoch 1 iteration 0 with loss 0.69373. Total time 0.0 hours\n",
      "Training at Epoch 1 iteration 100 with loss 0.24585. Total time 0.0025 hours\n",
      "Validation at Epoch 1 , AUROC: 0.72440 , AUPRC: 0.25495 , F1: 0.27722\n",
      "Training at Epoch 2 iteration 0 with loss 0.09313. Total time 0.00444 hours\n",
      "Training at Epoch 2 iteration 100 with loss 0.01475. Total time 0.00666 hours\n",
      "Validation at Epoch 2 , AUROC: 0.72943 , AUPRC: 0.24917 , F1: 0.26966\n",
      "Training at Epoch 3 iteration 0 with loss 0.01184. Total time 0.00861 hours\n",
      "Training at Epoch 3 iteration 100 with loss 0.04135. Total time 0.01111 hours\n",
      "Validation at Epoch 3 , AUROC: 0.73303 , AUPRC: 0.27552 , F1: 0.31578\n",
      "Training at Epoch 4 iteration 0 with loss 0.00597. Total time 0.01305 hours\n",
      "Training at Epoch 4 iteration 100 with loss 0.09914. Total time 0.01527 hours\n",
      "Validation at Epoch 4 , AUROC: 0.69888 , AUPRC: 0.25823 , F1: 0.30769\n",
      "Training at Epoch 5 iteration 0 with loss 0.01548. Total time 0.01722 hours\n",
      "Training at Epoch 5 iteration 100 with loss 0.03572. Total time 0.01972 hours\n",
      "Validation at Epoch 5 , AUROC: 0.72719 , AUPRC: 0.22114 , F1: 0.22222\n",
      "Training at Epoch 6 iteration 0 with loss 0.00811. Total time 0.02166 hours\n",
      "Training at Epoch 6 iteration 100 with loss 0.03094. Total time 0.02388 hours\n",
      "Validation at Epoch 6 , AUROC: 0.69107 , AUPRC: 0.20544 , F1: 0.24528\n",
      "Training at Epoch 7 iteration 0 with loss 0.11622. Total time 0.02611 hours\n",
      "Training at Epoch 7 iteration 100 with loss 0.00033. Total time 0.02833 hours\n",
      "Validation at Epoch 7 , AUROC: 0.68711 , AUPRC: 0.21855 , F1: 0.27160\n",
      "Training at Epoch 8 iteration 0 with loss 0.00479. Total time 0.03027 hours\n",
      "Training at Epoch 8 iteration 100 with loss 0.00167. Total time 0.03277 hours\n",
      "Validation at Epoch 8 , AUROC: 0.69214 , AUPRC: 0.18400 , F1: 0.29213\n",
      "Training at Epoch 9 iteration 0 with loss 0.01625. Total time 0.03472 hours\n",
      "Training at Epoch 9 iteration 100 with loss 0.02266. Total time 0.03694 hours\n",
      "Validation at Epoch 9 , AUROC: 0.71376 , AUPRC: 0.26114 , F1: 0.27659\n",
      "Training at Epoch 10 iteration 0 with loss 0.01236. Total time 0.03888 hours\n",
      "Training at Epoch 10 iteration 100 with loss 0.00125. Total time 0.04138 hours\n",
      "Validation at Epoch 10 , AUROC: 0.72438 , AUPRC: 0.23710 , F1: 0.36363\n",
      "--- Go for Testing ---\n",
      "Testing AUROC: 0.7898595968813537 , AUPRC: 0.4240506983064935 , F1: 0.43478260869565216\n",
      "--- Training Finished ---\n",
      "model training finished, now repurposing\n",
      "repurposing...\n",
      "in total: 82 drug-target pairs\n",
      "encoding drug...\n",
      "unique drugs: 81\n",
      "drug encoding finished...\n",
      "encoding protein...\n",
      "unique target sequence: 1\n",
      "-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n",
      "protein encoding finished...\n",
      "Done.\n",
      "predicting...\n",
      "---------------\n",
      "Predictions from model 4 with drug encoding Daylight and target encoding AAC are done...\n",
      "models prediction finished...\n",
      "aggregating results...\n",
      "---------------\n",
      "Drug Repurposing Result for SARS-CoV 3CL Protease\n",
      "+------+----------------------+-----------------------+-------------+-------------+\n",
      "| Rank |      Drug Name       |      Target Name      | Interaction | Probability |\n",
      "+------+----------------------+-----------------------+-------------+-------------+\n",
      "|  1   |      Efavirenz       | SARS-CoV 3CL Protease |     YES     |     0.57    |\n",
      "|  2   |      Remdesivir      | SARS-CoV 3CL Protease |      NO     |     0.23    |\n",
      "|  3   |      Zanamivir       | SARS-CoV 3CL Protease |      NO     |     0.20    |\n",
      "|  4   |      Letermovir      | SARS-CoV 3CL Protease |      NO     |     0.13    |\n",
      "|  5   |   Podophyllotoxin    | SARS-CoV 3CL Protease |      NO     |     0.11    |\n",
      "|  6   |     Methisazone      | SARS-CoV 3CL Protease |      NO     |     0.06    |\n",
      "|  7   |      Tipranavir      | SARS-CoV 3CL Protease |      NO     |     0.02    |\n",
      "|  8   |      Atazanavir      | SARS-CoV 3CL Protease |      NO     |     0.01    |\n",
      "|  9   |     Elvitegravir     | SARS-CoV 3CL Protease |      NO     |     0.01    |\n",
      "|  10  |       Loviride       | SARS-CoV 3CL Protease |      NO     |     0.01    |\n",
      "|  11  |      Baloxavir       | SARS-CoV 3CL Protease |      NO     |     0.01    |\n",
      "|  12  |     Enfuvirtide      | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  13  |     Nitazoxanide     | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  14  |      Indinavir       | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  15  |      Darunavir       | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  16  |     Dolutegravir     | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  17  |      Amprenavir      | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  18  |      Pyrimidine      | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  19  |      Doravirine      | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  20  |      Vicriviroc      | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  21  |      Foscarnet       | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  22  |    Fosamprenavir     | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  23  |     Delavirdine      | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  24  |      Pleconaril      | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  25  |       Arbidol        | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  26  |     Taribavirin      | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  27  | Tenofovir_disoproxil | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  28  |     Bictegravir      | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  29  |     Rilpivirine      | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "|  30  |      Cidofovir       | SARS-CoV 3CL Protease |      NO     |     0.00    |\n",
      "checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ldHx8XuPKFUVa2kQsFguzZvVm1qzeRd62qFy6WKWB8PBwpSMAajSFw/Cfh7Px2EZPi1Egap5TvDfnSt4FXZDkDyeq2hmhq4lhc6gK5yrh0t4A0DtqaJaxuohRSvHX4QS+/P0wF5LTAOjfvyVLlz6Yr3pEJFIpFV4QGXSPQ6PR2CitSgOggvEMJTEAfmnn+iGfyWWLOMxaulQh72NcUVxK43RiCl9sOsTe2Kyhs0cfbcOnn95XLO1nohWHRqPJwe7Hd19dBRHmorRcDNj2xt7CIDUqiiPz7qNmaHMmzl9RKHW6iytjdWFSqdLrXLqUNX+3Ro0KxMQ8R0CApcjadIVWHBpNOSXj8mVUaqpDWsUUY+j6Sm6Lz9whzXx7z6WeSmnJhdOWSUZSUqHUU1K5csX4bURg/vy+PP74tR6TRSsOjaYckWnDqJegmLrgSo4FaPPN7/0zO15lS3WMr69d1/NFZls/vHyVbeXNqvd2cnTPmSJvpzBZvHgXn3zyF7/88jgAe/Y8zeDBK/n1V88vcdOKQ6MpR2TaMBqcUvhbId0LUn2zFVJeqAy/ArcRiOF5NR1vLuGfa1mLlxDgV4iPIRGq9M45q6g4lEaD1jUKpZ7kZCsNGrxDQkIKAN98E0W/fi1o1CioRCgN0IpDoymXTO86nePfjqP6Hb3p7HsrUHi2BnfsG56iKG0QhcHgwd8wf/5Oh7TDh897SBrXaMWh0Wg0Hmbr1mN07jzP5lsKIDS0CgcPPovFUvzG77zQikOjKWXkZ61Fg5OKu//MwMd8Ho0208+dGgn4wp6vib7pEyMxorAlLVpKo93CFR06fGzb9vISVq0aSO/eTTwoUe5oxaHRlDLys9aiz7YMuv6dc5FvCoZhw9u/4AvmcqXJ7UVTrx35VRqFZYMoCjp3rs+mTbH06dOY779/xNPi5IlWHBpNKcV+rYWrNRHH9jzPhV0rqfbwwwSEX28kfmkaWAd+RqWbboKKFYtF3qKipNstspOQkMw118wiOdlKeroxo+y33waTnGz1yJqMguDlaQE0Gk3RU6F9O6r06WN8QlONT8+eeJVypVHauPvuzwkOfpOLF9O4ckXx1FPf2fJKi9IA3ePQaIoddeUKKbt2oS6nFej4VkcNg8WlzX/a0tqePpgjDSA94XQBpdQUJqtXH+Cuu5aQkZE1bNiyZTAffdTXg1IVHK04NJpi5szcuZye9W6Bj8+M+Bnz+RO2tDcy0zbNcX7QiqGw/dECt5lfypLh+mpp0OAdYmIu2PYtFi82bRrMDTfU86BUV4dWHBpNMWONPwGAb4MG+NSqlSP/j8POH7jeAYdt2+pKBTIu13HIrxZgoXltu8BA0YYR3dsvg4q1HV2LFLXxuriURkk2eGcSG5ulNB5/vC0LFtzrQWkKB604NBoPUX3QIKoN6J8j/RYXhu42C9sA+XBAWAIW4pU2w3VhcOhQAvPn72LyZOPclyy5j5Ejf+TIkdGlyo6RG1pxaDQaTSHRqdPHbN58DIChQ68nNDSQ/v3b0L9/Gw9LVrhoxaEpVwyav4V1+wrPYNy44hxG/nKQipfdP6bqJQgAXtv8Gj9fnpwjv3IL47vNwgn5lsfRtmC6Fn/ml3zXo8kf8+ZtZ8iQlQ6hx+fOjbT1OsoaWnFoyhWFqTQAwk8cpkl8/o/LAI7VyH/goC71co8jXZIM0qXB/nC1JCdbqV//bc6ezbIhVajgwz//PEtoaKAHJStatOLQlEsKy6HfhBHPAxDYrx81hg51+zjvKpVZHhRUKDI4Y0Tte0ukk8GyRsWKrzvsT5jQmalTb/OQNMWHVhwaTSHgHRiI3zUNPS2GppipVasiJ09eIiwskP37R5RIh4RFgVYcmlKH9eRJLq5Zi7qSnu9j+x2MAuDMgsIZsmoZk9MPlKbs0qLFbE6cSOLcuf8AEBs7is2bj9GlS5hnBStmRKnSfeOHh4erbdu2eVoMTTFybMxYLvzwQ6HWubPNMM7UaF2odXoSPVRVuEybtpEXXsiaZDBpUpdSb/gWkUilVHhBjtU9Dk2p48rFiwBUuvVWfEPyt/r2k9+OAPDkzY7DSmeOlh2l0cBvW7F4py0PxMcn0rTpbJKSsmLsVqniy/jxVxtat3SjFYem1FKt/0NU6to1X8fMNRfXTXwhm3HcnLKaY8FaRCBtGoYC+Vh453FK95twSaF378/48cdDtn0RePvtXoweXb6VBmjFoSmFqHRr3oU0mqvEXmm0bl2T3buHeVCakoVWHJpSxaXNf5L8x+Yc6QVZ2OfUEV+E49z74bWC8y2jpnRitVr57bc4unc3hjFHjuzABx9sY8uWIbRvXyePo8sXOh6HplSRsmuXbdu/ZUvbdn6URvdmhjLIrjQa+OWcZLExoAKQ98I7Telm1KjV+Pq+To8en2K1Gj3aWbN6k5b2klYaTnC7xyEiFqAX0AiYr5S6ICL1gUSl1IXcj9ZoCpcaTw3Bx8kCuoIs7Bsxp4drh4CmY8H3b3s/3/VqSj779iXQtu0HpKVl2NKmTNlIRIS2E+WGW4pDRMKAtUAtDDc7K4ELwFigAvB00Yin0Wg0RUN4+FwiI7P8xYjAF188wP33t/KgVKUDd3scs4BNwFPAWbv0FcAn7jYmIneYdXkDHyulpmXLDwUWAlXNMhOUUoU7YV9TIkmLO8aZjz5CpabkWm7/HzuoBXyw/hDzT3/vsly+AgnZ2TWG/zycjcc2unecplRitVrx83vdwSHhLbc0YP36JzwmU2nDXcXRGeislLKKODhmOwrUdacCEfEGZgM9gThgq4h8p5Taa1fsRWC5UuoDEWkJ/ACEuSmjphRzfvlyzi9blme5zLBHib45Y2Vn2i7AfWd/DnaNJrc7VRravlG2sFgs+Pn5kJqaTkCAD1FRZdshYVHgruLwNj/ZCQEuullHB+CgUuowgIgsBe4B7BWHAjJDmAUCx92sW1PKUaZBskqf3lTMZW3G2OU7SfX2ZdHHY3jbzy/Pel0GEnKwaTyflZ7fYEmaUsHDD3/J33+fZudOY0rtnj1Ps2jRLm3LKCDuKo61wL+BzInMSkQqYoQ//tHNOuoBsXb7ccCN2cpEAGtE5N9ARcCpm0kRGQoMBQgNDXWzeU1pwL9Va6r26+cy/3+bDSdyXm4oDY1m48Zounf/lCtXjHGpb76Jol+/FjRqFKSVxlXgruIYB6wXkV2AP/Ap0BSjt/Gom3U4Cz6Q3VHWQGCBUuotEekELBKR1kqpDIeDlJoLzAXDV5Wb7WtKCOlnzpB2NMYx7eRJD0mjKYtYrVZatvyAgwfP2dK8vYUaNSp4UKqyg1uKQykVIyJtMZTE9RjrP5YBC5VS7g5VxQH17fZDyDkU9SRwh9nmHyLiDwQBp9xsQ1PCybh8mUO9+5BxwcUMbi+vq4rSl90onhmnOwemGxFc5WtKLVOmbODFF9c5pD3wQAu++OIhD0lU9nB3Om4HIFIp9UG2dG8R6aCU2uJGNVuBJiLSEDgGDAAezlYmBrgVWCAiLTB6N4Ubsk3jUTIuXjSUho8PFVo7Ohb0qlKZyj1vY917O3Otw94Inh17pXG06t8FklEbw0svVqvVQWlUrepHdPRIAgMDPChV2cPdoao/gDrkfPOvauY5M5w7oJRKF5FngZ/M8vOUUn+LyGvANqXUdxjrQj4SkecwhrGeUKXd77vGKd6BgYQtXeIi11AcVxOlb06nUex+fDfT+XdWoqtFfppST3KylYAACxaLhc6d6/P777HMnt2bYcM6eFq0Mom7ikPIaY8AqAYku9uYuSbjh2xpL9tt78WY+qvRaDR58tVXf/Pgg18CkJHxCgC//TbYkyKVC3JVHCKy3NxUwMcictku2xtoB+T0OKcpt5z5+GMSv1vpMl+lZ0XtuxpbRuZCvd5RQ2lw3sVK3wg9N7+sYrVaqV9/FidPXrKlvf32H4wZ08mDUpUf8upxXDG/Bciw2wdIARYDH2Q/SFN+ObNwIVdOJ+RZzjcsLFelkZsdA7At1HOmNI5W/ZsuyS5WoOsAR6WeYcNWMWdOpEPaiBE3aKVRjOSqOJRSAwFEJBqYrJS6lFt5jSZzQDN0wXy8q1VzWcy3YUN4eS1wdbaMTBwX+vWAiBeNTW3PKDNYrVYqVZrm4JCwdu1KxMSMxGKxeFCy8oe703FfKGpBNGULv0aN8AnWsSw0hYfFYsFqNZSGCHz99UP069fCw1KVT/LjVn0gxgK9UMDXPk8p1dLpQZoyR9KGDaTFxbnMVym5OynUaPLD7Nl/8uefx/j00/sAWLLkPj766C9+/vkxD0tWvhF3ZruKyGjgVWAehtuRj4EmGC5D3rWfGVXchIeHq23bcgbg0RQ+lw8f4XCfPm6VfbDPayT5up47X6H+fHwq7SuQHM4M4iNq3+u8sB6qKpUkJibToMG7JCYa83GOHh2tHREWMiISqZQKL8ix7vY4hgFDlVLLRGQI8LZS6rC5BkOPR5QTMi4YD2HvGjWofHtPl+VeibqSq9IACqw0IKdB3FnkPkAbwkspDz64nC+/jHJI++OPGEJD9Sr/koK7iqM+WdNuU4DK5vYiM11HcS9HWELqUeeVV1zmr5lgxMnIzejdZuEEoGBeaGf/8QuQR+Q+Talj3boj9Oy5yOaQEKBx42rs3TtMG79LGO7GHD8JVDe3YzBcpAM0wLnzQo1Go3Ebq9VKjx5ZXmx9fIQNGx7nwAE9Y6ok4q6NYx5wRCn1f6bL82nABgwbx7dKqUFFK6ZrtI2j6MhXFD0P4GDX0D2OUk/t2jM4efISAwe24vPPH/C0OGWe4rBxPJNZVin1XxG5gOEa5H/AfwvSsKbkU5KVRvbIfZrSRUxMIi1avIeIkJQ0EYDY2FEkJ1u1Q8JSgLvrONKANLv9hRixwTXlgMzFdTHdGnLphD/1P/qISl1udlo2zC37RmFE2euBQ+Q+TamhW7cF/PrrUdv+vHnbGTz4OiwWC4GBeliqNOCujcMpInKXiGwvLGE0Gk3ZZdmy3Xh5veqgNG64oQ6DB1/nQak0BSHPHoeIPArcDliB95RS20WkIzATuBZYWrQiaooSd+wYkb2vpa6c4XKiPwBPrZzMnsPHnJatbC7kzZw1pdFYrVZCQmZy6lSWI20/P2927nyGZs2CPCiZpqDk2uMQkVEYi/6uxVg1/quZ9gOwDmiolHq8yKXUFBl5KY0aZ/YQcCSV84crkp5ihF1Jru565bi76GBJ5Qt7pTFyZAdSU1/USqMUk1ePYyjwrFLqQxHpiRGE6T6gqVIqbxeomlLDiDk9OPJQf1J37WJ5k+6M/Fc3M6c2+J03Nu+ehU+tmhyOfha4WhuFpiyzY0c8f/wRy7BhHbBYLIwc2YEvv9xLdLSeXlsWyEtxhAE/Aiil1opIOjBBK42yze91WvNKf7v4zFFPGd+ZaQv10h2Na9q1+4Bdu4xgoUOGXIvFYmHWrN7MmtXbw5JpCou8jOMVMFaKZ3IZYzGgppSjlOLyoUO2/ZS//0aluB3MUaPJwcyZmxF51aY0AL7++h8PSqQpKtyZjvuEiCTZlf+XiDj0OJRS7xe6ZJoi5ZsXVnP8vL9tP/r+rAVX3/i9zPAP3mZjQAUjoWGo8b1Q+wrS5CQxMZnQ0FlcuGCbsU+lSr4cOTKKoCC9JqMskpfiOAU8Z7d/npx+qRSgFUcpw15pBF+O5mBgPQCurXwQ/6rWLKXhAm3c1gAkJ1upWvVNh7SpU3swYYK+P8oyeUUArF1cgmg8w8CbjlH9scGETagFQLT/ww752gCuyY2AAAv+/j6kpqbTvHkQUVEjPC2SphhwO5CTRqPRWK1WGjeejcUiHDw4CoA9e57m9OkUOnas72HpNMWFVhxljPw6JoxY+Tff7TXchMyzTC8qsTRlgBde+Jlp0zbZ9r//fh933tmMRo2CaNTIg4Jpih2tOMoY+VEaNc7s4UTFGrb9Ht47jI0mt0O6ng2jMTh0KIHWrT8kNTXdllajRgW6d7/Gg1JpPIlWHGWUEXN6cH7FN8S/8AIVu3ZhiFwLwLcjOtvKeFfpy4CwsKyDIszvR77QM6g0ANx88zw2bYq17YvA/Pl9efzxaz0olcbTaMVRDvCpVp39YkyprdC2rYel0ZQWEhOTHZTGTTeFsGnTkx6USL4sDQAAACAASURBVFNScFtxiIgF6AU0AuYrpS6ISH0gUSl1oagE1OSNslq5uH49GRcuANUAOP/VVyRv146LNfkjOdnK/v0JtG9fh8DAALp0qc/WrfHs2fM0jRpp31IaA7cUh4iEAWuBWkAAsBK4AIzFWF3+dNGIV37Jf/Q9bzKVBkD8pBdt20t3nDDcVAIsfhAOrHFaw/Bawcb6DT1MVS554okVLFy4CxHIyDBiym/YMNjDUmlKIu72OGYBm4CngLN26SuATwpbKM3VRd/LSI5lTegNAFi9fPimkbEYq3uzYJdKA8ix6E8v8isfbN16jM6d52G1ZgCgVNaMKY3GGe4qjs5AZ6WUVcTBwd1RoG6hS6WxkRl9zxVhE77nrsObGLFrBdUeHkjtl182c7K83Y+zPyAi89tJjO5CicynKU20bDmbqKgsD0JeXsKqVQPp3buJB6XSlHTcVRze5ic7IcDFwhNHo9EUBwkJyQQHO7oKueuuJqxc+bCLIzSaLNxVHGuBf5Plp0qJSEXgFUy36+WZ/Nsj3GfQ/C2c+n0LL/05nyrWZESUQ/5qMLyFIbDlY4h4K886h9cKZqO2Y5RrgoICEDGGpSpX9uXwYe2QUOM+7sYcHwf0EpFdgD/wKXAYaAj8x93GROQOEdknIgdFxGlsURF5SET2isjfIvK5u3V7kqJSGg1a12DdvtO0SThEYFoyooAMyflRgngpAmpedqve3BwYartG2eXOOxdz552LbftffPEAb711OxcuvKCVhiZfuNXjUErFiEhb4DHgOgyFswxYqJRya6hKRLyB2UBPIA7YKiLfKaX22pVpAryAYU85JyI183U2HiYve0RBeHZCVpjWGi0uEtz6Irx0OmdBLy/Ex80OpLZllCtWrz7AXXctISPD6K3GxCQSGhrI/fe38rBkmtKKu9Nxq5hrNa7GfXoH4KBS6rBZ51LgHmCvXZmngNlKqXMASqlTOWopzwiIN+Dr62lJNKUAq9XKNde8R1xc1jIri8WLCxdSgUDPCaYp9bhr4zgpIquARcAPSqn0vA5wQj0g1m4/DrgxW5mmACKyCcMYH6GUKtE2lFXv7SzwsSk7d5K46ntjoBn4OeokcedSHMo8A/Q+v/lqRLQx/OfhbDy2sVDq0pRsxo37ibfecrxvBg1qx7x5/TwkkaYs4a7i6A88DCwBUkTkC2CRUur3fLTlLFC1yrbvAzQBumHM2NooIq2VUucdKhIZCgwFCA0NzYcIhU+mfaNB6xp5lMzJyanTSNmxw7Z/vflxhbdvhuGAsIDYKw1tyyi7xMcnOiiN4OAAoqNHExBg8aBUmrKEuzaO74DvRKQScD+GEvlVRGKBz5RSL+dagUEcYO+wPwQ47qTMZqWUFTgiIvswFMnWbPLMBeYChIeHZ1c+HuGuZ9vl+5iMy4Yxu/qTg7HUqk3Eyr8BiLg729jzj//ByyeDKh8cgooVr1pWbdsom1itViwWC3XqBFK7diVOnkxi0aJ7eeQR7Z9MU7jky8mhUioJWAgsFJEWwOfAJMAdxbEVaCIiDYFjwAAMBWTPN8BAYIGIBGEMXR3Oj4ylkcA778S/ZUtbXIx3H7vTscDhZ43vQlAamrLHvHnbGTJkJX5+PqSkTAIgPn6sh6XSlGXypThExA+4G3gE6A2cBma4c6xSKl1EngV+wrBfzFNK/S0irwHbzF7NT8DtIrIXuAKMV0oVzVxXD5KekMCV8+dRqameFkVTiklOtlK//tucPWvcR6mp6WzcGE2XLmGeFUxT5nF3VtWtGMriPjPpa6APsE4p5fZQkVLqB+CHbGkv220rYIz5KXEUxkK/1H/+4ch990NGhi2tz7u/cbjqEceCuTgjzA/aIF42eeSRr/j88z0OaZMmddFKQ1MsuNvj+AGjNzAU+FYp5d5KszKGK6WRH8N4WvRRyMgg2cePhAqBHKsYzNEqtW353ZsFGxvZlUYBjeLZlYY2ipdu4uMTCQ2dRXp61vtaw4ZV2bdvOBaLNn5rigd3FUcdpdTZvIuVDwpjod/2mk2Z0uFxoqfdSa6Oq505IywA2iBeNggKCrApDW9vYfXqR+jZUwf81hQvLhWHiAQopZLN3VQRcemTwK6cRqMpZKZN20hqqpWIiB5YLBbGju3IoUPnWLFigKdF05RTcutxXBSROubq7SRyrrmwx5nn3FJLUTgtPP/8bST+cZj0y16Ahd5eW3jK/6csN+f5RNsuyj7x8Yk0aTKbS5esgGHDsFgszJjRy8OSaco7uSmOPmQFbepD7oqjTJGb0ijIQj+AM+ujSbvgZ9u3VLyS90G52DXyozS0XaP00avXItasyZqJLgI7dpzihhvqeVAqjcbApeJQSv1kt12i3X4UFYXqtFAZC+frTn+D/qti2V+1Pocj+l51tdp2Ubb4/vt99O27zOaQEKBt25rs3Dksl6M0muLF3em4yUADpdTpbOnVgTillPbJ7Cb+rdvwz+9lamRPU0gcOpTAXXctte1bLF5s2TKE9u3reFAqjSYn7s6q8se5ryl/3I/pUaZI2bmTtJjYvAuaXLE6u3waTRaNGgXh7+9Damo6Tz11HXPn3u1pkTQap+SqOERkuLmpgCdEJMku2xu4BdhfRLIVO+4axa3x8UT3z++MFqOX0Xvl3VRuYSiRNgudxrLSlBP27UugbdsPqFmzErGxzwEQG/scgYEWvSZDU6LJq8fxkvktwFggwy4vDYgGhlNGsFcauRnBr5w7B4BXlSpU6uLE8Lz7C6fHfRhWidOFGAZBG71LL+Hhc4mMjAcgLu4Ce/acpHXrWjoSn6ZUkKviUErVARCRP4A+mQGWyjruGsUt9epR7y0nrroiPjK/HRfvrTAj712Mmkb0tDuzH6UpB3z00Xaefnol9o56uncPo3XrWh6TSaPJL+66Ve9U1IJoNGWZxMRkwsL+y/nzWY4tAwJ8iIp6ltBQHY1PU7rIbeX4dOBVpdQlc9slSqnnC12yYsatSH6LH+Tcj79xakcVwAvid0FE1p9+eK1gNgZUgIZmcCmzh6HRJCZaHZTGK690ISKi8GPUazTFQW49ji6AxW7bFWViYaBbkfwOrCHpWHUyrMZEsoBgR1+PGwMq5NlOelKzLEeGmjLNxo3RJCVZ6d27CaGhgXTvHkZMzHmiorRDQk3pJrcFgJ2cbZd13I3kV2/mO1S54w7HRLOH4WpRXtgEI1DT/GkdCi6gpsRjtVpp0eIDDh06hwhkZLwCwC+/PO5hyTSawqHAazBEJERE8hUIqiwhfn55F9KUO157bT2+vq9z6JAxj0QpY9qtRlOWcHfleARwUCn1mbm/CtOXlYjcoZTaVnQiFj3u2jcAkk/7Os0e/nPWrORB87ewbt9pp+U0ZZP4+EQaN36P5OR0W1rVqn5ER48kMFBPsdWULdztcTwBHAIQkV5AJ6Ab8AUwrSgEK07ctW8ANvuGl7+/Q3am08Eu9brkqjS0faPssWNHPHXrzrQpDRF4//3enDs3QSsNTZnE3aGm2kCcud0H+EIptUFE4oEtRSKZB3DHviEBAajkZPzbOJ8x9f5t7xP2s2HL0Gs1ygft29dBxBiWuu662kRGPu1pkTSaIsVdxXEWCAFigV44rigvlx77RLTvqfKK1Wqlfv1ZtGwZbDN4//TTv6hTp5JeyKcpF7irOL4BPhORKKAmkOlmvT1wsCgE8xRKKazHjoPKcMxIMvVjRkbOgzTlhmHDVjFnTiQAJ09eIiYmkdDQQB2+VVOucFdxjAbGA6HAHUqpi2Z6A+DjohCsqMjVkeHiB1n+zU7a7nbWicp8kzQWcXX4/EYu++bsdWROudWULfbsOcn1188lLS3rxaFu3UrUqaNtGJryh7suR9KAKU7S3yx0iYoYV0qjQesacGANvufqAnC2ElhdXJ299cWp0khPambb1kbwssO1185hx46Ttn0R+Prrh+jXr4UHpdJoPIfb6zDMoE3PAC0xVov/DcxVSp3N9cASilNHhhFZm9d++BkB11/v9Nhu5HQJnNnT0AbxssXmzbEOSuPWWxvy88+PeVAijcbzuDUdV0RuxJiO+wzghxHAaThwUERuKDrxNJriJzExmfh4w7Nxx471CQmpQkCAhePHR2ulodHgfo/jLQwD+VNKqXQAc9X4x8A7wM1FI17R0caJA8Lr0kOYEGeMYT845w/+rnGiuMXSeJgHHljGV1/9g7+/DykpkwBsQZY0Go2Bu4rjemBIptIAUEqlm15zS/WqcXvu2Zxl+DzjXyXfx2u7Rull3boj9Oy5iCtXDJ+dqanpxMcnUqeOdnmu0WTHXcVxEagP/JMtPcTMK3XsPhJjbNgFW4pePZCUuB28c+1DbP6vHpIoD1itVpo1e58jR87b0nx8hI0bB2mlodG4wF2XI8uBT0TkfhGpIyK1ReQB4CMzr0wRW6mmp0XQFAMbN0bj6/u6g9IYOLAVVuvLdOxY34OSaTQlG3d7HOMwYnMsJUvZZGDYOMYXgVweIWXPHk+LoClGOnasZ9uuVs2fuLgxBAToOBkaTV64u44jFXhaRP4DNMFwNbJfKXU+9yNLDsN/Hk6ln1vQgFZO81P37YN0w4RzxavA3uY1JZxbbpnPDTfUZcaMXlgsFiZP7k6dOpUYPPg6T4um0ZQa8lQcIlIXuBWjx7FBKbW1yKUqAjYe28gz5x8A4GLt+Bz56aezYiaEdLi22OTSFA/Llu1m4MCvUQo2bIhh6tQeWCwWJk3qmq96rFYrcXFxpKam5l1YoykB+Pv7ExISUqhRJ3NVHCJyE/ADkDnFKE1E/qWU+rLQJPAAEyIegYjsS/gMtgc3Zd6THYtZIk1RkZxspWHDmZw6lWxL8/f3JjHRSlBQ/v9IcXFxVK5cmbCwMO3oUlPiUUpx5swZ4uLiaNiwYaHVm9eYzGRgM9AYYwbV58CMgjYmIneIyD4ROSgiE3Ip94CIKBEJL2hbGs1TT31HxYqvOyiN0aNvJCXlRYKCCuZjKjU1lRo1amiloSkViAg1atQo9B5yXkNV7YDuSqnDphCjgPMiUjW/9g0R8QZmAz0xYntsFZHvlFJ7s5WrDIwE/sxP/fY4c2T4DLNs2zvf6EmrVC9i1tfg5NeGbrJkXKFiQRvUlDjWrj3Exx//ZduvV68yR478u1C661ppaEoTRXG/5tXjqAbYlk+bXnGTzfT80gEj/Oxh02niUuAeJ+X+D5hOphvaAuDS+y2GM8N2KVtIOWPh8nkLVdMuUTXtEhXTjeZSGzcvaLOaEoDVagWgZ89G+Pv74OUlrFo1gLi4MYU6xqvRlGfcmVXVVESC7PYFaCIiFTITsvcaXFAPIxBUJnHAjfYFRORaoL5SapWIjHNVkYgMBYYChIaGumxwTqdRAOx+fLdjRkTWZsXOnak7/Q1jx8uLFtUKohM1nmbmzM0899xP1K1biWPHxgJw4cLzWlloNEWAO/NOfwV2230CMAI57Qb2mN/u4Ky/pGyZIl4Yfq/G5lWRUmquUipcKRUeHHx1bj7E1xefGjWMj1YapY6EhGQCA6fy3HM/AXD8eBKJiYZNo6wqDRHh0Ucfte2np6cTHBzMXXfdBcCCBQsIDg6mffv2tGzZko8++siW7uXlxa5du2zHtm7dmujoaADCwsK4//77bXlffvklTzzxRK6yrF69mvDwcFq0aEHz5s0ZN85434uIiCAgIIBTp07ZylaqVMnhHMaOzfqrz5gxg4iICKdtfPPNN7z22mu5yuFJlFKMHDmSxo0b07ZtW7Zv356jzMWLF2nfvr3tExQUxOjRowE4evQot956K23btqVbt27ExRlRuk+fPs0dd9xRrOfiLnkpjhYYbtTtPy3s0jO/3SEOw21JJiHAcbv9ykBrYL2IRAMdge/yYyCfFrGY2c/84jJ/5xs9ISKQi8f8SIzWAXhKO/fcs4Tg4De5cCHNljZ9+m0EBpbt37ZixYrs2bOHlJQUANauXUu9evUcyvTv358dO3awfv16Jk6cyMmThmv4kJAQpkzJEVrHxrZt2/j777/dkmPPnj08++yzfPbZZ0RFRbFnzx6uueYaW35QUBBvvfWW02P9/Pz4+uuvSUhIcJpvz/Tp0xk+3PksSGekp6fnXagQWb16NQcOHODAgQPMnTuXYcOG5ShTuXJlduzYYfs0aNCA++67D4Bx48bx2GOPsWvXLl5++WVeeOEFAIKDg6lTpw6bNm0q1vNxh1yHqpRS+wqxra0YQ1wNgWPAAOBhu7YSAduQmIisB8Yppdx2olj5RB3b9tGqxs3fpV4XW1q7lC1kpAtxv1UHZXSAvCpqk3hpY+vWY3Ts+AkZGbYOK82bBxEVNaJY5SiqaI/uxHTp3bs333//PQ888ABLlixh4MCBbNy4MUe5mjVr0qhRI44ePQrAXXfdxYYNG9i3bx/NmjXLUX7cuHG8/vrrLF68OE8Zpk+fzqRJk2je3LAL+vj4ODzgBw8ezIIFC/jPf/5D9erVHY718fFh6NChvPPOO7kqsv379+Pn50dQkPFoWLlyJZMnTyYtLY0aNWqwePFiatWqRUREBMePHyc6OpqgoCAWLVrEhAkTWL9+PZcvX2bEiBE8/fTTJCUlcc8993Du3DmsViuTJ0/mnnucmVrd59tvv+Wxxx5DROjYsSPnz58nPj6eOnXqOC1/4MABTp06RZcuxrNp7969vPPOOwB0796dfv362cr269ePxYsX07lz56uSsbAptiXSpmfdZ4GfgChguVLqbxF5TUT6FmZbI+b0YPq0f7P78d28f9v7jnJcAZQgfn4EP/ccwWZ3UVN6qF7dz6Y0LBYvtmwZUuxKw9MMGDCApUuXkpqayq5du7jxxhudljt8+DCHDx+mcePGAHh5efH888/z+uuvOy3/0EMPsX37dg4ePJinDHv27OF6F8HOwBiaGjx4MLNmzXKaP2LECBYvXkxiYqLTfIBNmzZx3XVZq/pvvvlmNm/ezF9//cWAAQOYPn26LS8yMpJvv/2Wzz//nE8++YTAwEC2bt3K1q1b+eijjzhy5Aj+/v6sWLGC7du3s27dOsaOHYtSKke7/fv3dxhayvx8+umnOcoeO3aM+vWzBlNCQkI4duyYy3NasmQJ/fv3t812ateuHV999RUAK1as4OLFi5w5Y0zwCQ8Pd/pC4GncjgBYGCilfsBYUGif9rKLst2KUhbx9yfo6aFF2YSmEHnhhZ8JD6/D/fe3olGjIPr2bUq1av4sWHCvx2TyZLTHtm3bEh0dzZIlS+jTp0+O/GXLlvHbb7/h5+fHhx9+6PDG//DDDzNlyhSOHDmS4zhvb2/Gjx/P1KlT6d2791XLOXLkSNq3b+9gz8ikSpUqPPbYY7z77rtUqFDBydEQHx+PvR0zLi6O/v37Ex8fT1pamsOitr59+9rqWbNmDbt27eLLL421yomJiRw4cICQkBAmTpzIhg0b8PLy4tixY5w8eZLatWs7tLts2TK3z9GZ4sltCuzSpUtZtGiRbX/GjBk8++yzLFiwgK5du1KvXj18fIxHc82aNTl+/LirqjxGsSoOT6GUIv30aUjxIiNN+6EqTRw6lEDr1h+SmpqOCGRkGL7Gvv12oIcl8zx9+/Zl3LhxrF+/3vaGmkn//v157733nB7n4+PD2LFjeeONN5zmP/roo0ydOpVWrZz7dcukVatWREZG0q5dO5dlqlatysMPP8z777/vNH/06NFcd911DBo0yGl+hQoVHHok//73vxkzZgx9+/Zl/fr1Dgb1inbDzkop/vvf/9KrVy+H+hYsWMDp06eJjIzEYrEQFhbmdHFc//792bcv50j9mDFjeOwxx5ALISEhxMZmTRiNi4ujbt26Ts9n586dpKenO/TU6taty9dffw1AUlISX331FYGBhkv/1NRUl0rVk5RNxbH4QTiwxrZ7/I+qXDgaANR2fYymxHHzzfPYtCnWIS0xMbnMG7/dZfDgwQQGBtKmTRvWr1+fr2OfeOIJpk+fzsWLOcPpWCwWnnvuOaZNm0aPHj1c1jF+/Hjuu+8+br75Zpo2bUpGRgYzZ85kzJgxDuXGjBnDDTfc4NRoXb16dR566CE++eQTBg8enCO/RYsWfPbZZ7b9xMRE20SAhQsXupStV69efPDBB/ToYfgk279/P/Xq1SMxMZGaNWtisVhYt26dzfaTnfz0OPr27ct7773HgAED+PPPPwkMDHRp38i0R9mTkJBA9erV8fLyYurUqQ7XYf/+/bRu3dptWYqLfL1+i0glEWknIiV7nqOd0gBIPesLgLffFVQFL3yCg6lqzmjQlDwWLvwLL69XHZTGTTeFkJHxilYadoSEhDBq1KgCHevr68vIkSMdpsva8+STT+Y5O6lt27bMnDmTgQMH0qJFC1q3bk18fE4HokFBQdx7771cvnzZaT1jx451Obuqa9eu/PXXX7bhoIiICB588EG6dOliM5g7Y8iQIbRs2ZLrrruO1q1b8/TTT5Oens4jjzzCtm3bCA8PZ/HixTbD/tXQp08frrnmGho3bsxTTz3l0Ltq3769Q9nly5fnUBzr16+nWbNmNG3alJMnTzJp0iRb3rp167jzTs8NibpCnI3P5SgkUhH4APgXRhyOpkqpwyLyHhCvlHI9LaKICQ8PV9u2GROvMqfijqhtjnub0f3WhnclJOk01/zwA37XFJ6jL03hs2zZbgYM+Nq27+/vw549T9OokeuHRHESFRVFixYtPC1GuWLUqFHcfffd3HbbbZ4Wpdjp2rUr3377LdWuco2Zs/tWRCKVUgXyB+huj2Mq0By4CUdXIGuABwvSsEbjjP7925BpV3z++U6kpEwqMUpD4xkmTpxIcnJy3gXLGKdPn2bMmDFXrTSKAndtHPcADyml/hQR+y7KXuAaF8cUG6temM/Rcw1ypA+av4Utu2P4Mum0B6TSuMPWrcfo3HkenTqF8OuvhoE0KmoE11wTWGZXfpc25s+fn2NKbefOnZk9e3axtF+rVi369i3UGfulguDgYIc1HSUJdxVHMOBsMLRErJ6zVxqZC/9ocjvrdp+my+msmRHe1aoWt2iaXGjZcjZRUcbY9oYNMSQnWwkIsNCsme5hlCQGDRrkctaTpnzi7lBVJGA/WTyz1zEY+KNQJboK5nQaxeoWcw3bxiNfAOCdkQGAX4sW2hdVCeHNNzch8qpNaQD07dtUx/vWaEoJ7vY4JgE/iEhz85gRItIK6AbcUkSyFSp+13h8RK3ck5CQTMOGs0hKyvItVaWKL4cOjSpwYCWNRlP8uNXjUEptwFAQNTH8TN0HXAI6K6W2FJ14V08la4qnRdCY/PbbUQel8dZbt5OY+IJWGhpNKcPtBYBKqUigfxHKUiQ8sXc1AOrKFQ9LUj5ZvfoATZtWo1GjIPr1a0GjRtXMKbbuezvVaDQlC7d6HCISkNunqIXMD/becAHO+xkxACrf6noFrKbwsVqt1K//Dn36fE7r1h/a0g8eHKmVxlVy4sQJBgwYQKNGjWjZsiV9+vRh//79REdHIyL897//tZXN9IEExmrxevXq2RbiJSQkEBYWlmtb+/fvp0+fPjRu3JgWLVrw0EMPcfLkSdavX4+IsHLlSlvZu+66y7aCvVu3boSHZy0R2LZtG926dXPaRnx8vC2WSEll4cKFNGnShCZNmrhcsW7vGDEsLMy2+C8tLY1BgwbRpk0b2rVr57DK/7bbbuPcuXPFcQqFirvG8STgYi6fEkN2b7iZVGjTppglKb8899yP+Pq+TlzcBQBSU9NtIV01V4dSinvvvZdu3bpx6NAh9u7dy+uvv26Lt1GzZk1mzZpFWlqa0+O9vb2ZN2+eW22lpqZy5513MmzYMA4ePEhUVBTDhg3j9GljentesT1OnTrF6tWr82zn7bff5qmnnnJLJoArxTx6cPbsWV599VX+/PNPtmzZwquvvur0Yb9s2TJbvI3777/fFm8jM5DW7t27Wbt2LWPHjiXDnLTz6KOPuvTjVZJxd6gqu5tMC3AtMAR4qVAl0pRa9u1LoH37OaSmZv2xa9YM4MiR0WVzTUZEYBHV69rN+Lp167BYLDzzzDO2tMw32+joaIKDg+ncuTMLFy50+jAePXo077zzjlsP6s8//5xOnTpx991329K6d+8OGG4y2rVrh9VqZe3atfTs2TPH8ePHj2fy5Ml5etn96quvmDx5su0cHn30US5dugTAe++9x0033cT69et59dVXqVOnDjt27GDv3r189tlnvPvuu6SlpXHjjTfy/vvv4+3tzbBhw9i6dSspKSk88MADvPrqq3mea2789NNP9OzZ0+ZhuGfPnvz44485XIdkopRi+fLl/PKL4cli79693HrrrYCh2KtWrcq2bdvo0KEDffv2pUuXLg5uRkoD7hrHf8r2WaWU+j/geTy8cjzxVE7j96D5Wwib8D3ByeeodynvCGOaq+ejj7bTvPlsm9IQgaVL7+PkyfF6mm0hklcMDIAJEybw1ltvOX0zDw0N5eabb3Zw6301bb344ou2h352OnXqhJ+fH+vWrXN5/JEjR6hWrRp+fn6A8WBdu3Yt27dvZ9myZYwcOdJWdsuWLUyZMoW9e/cSFRXFsmXL2LRpEzt27MDb29sWfGrKlCls27aNXbt28euvvzqEys3kzTffdBpvw769TPIbb2Pjxo3UqlWLJk2aAEa8jW+//Zb09HSOHDlCZGSkzZtutWrVuHz5cg7vxiWdq/WOuw1wr99bRFxOMRyx2Rb+Aev2GV3p1mcO29J8atYsXsHKGU880YahQ43x7i5d6rNhQ05Pp2WOXHoGnqRhw4Z06NCBzz//3Gn+xIkT6du3b6E4z8uMYucq2FCmYnHlwj17vA2r1cqzzz5rUwb79++35XXo0MEWf+N///sfkZGR3HDDDQCkpKRQ0/yPL1++nLlz55Kenk58fDx79+6lbdu2Du2OHz+e8ePHu3WO+Y23kd0D7uDBg4mKiiI8PJwGDRpw00032eJtQFbMQpRcuwAAIABJREFUjRo1arglT0mgwIpDRHyBERjTcz3O6hZzc6TN7N+e45FLqNyzJ14BJcqGX+pJTrZSv/7bPPZYO9555w4sFguffHI3t93WiNDQIhrC0dCqVStbcKLcmDhxIg888ABdu3bNkde4cWPat2/P8uXL82zr119/zbOtSZMmMWXKFIeHYSY9evTgpZdeYvPmzU6PrVChgkM8jHfeeYdatWqxc+dOMjIy8Pf3t+Vlj7fx+OOPM3XqVIf6jhw5wowZM9i6dSvVqlXjiSeecBpv480333QaHrdr1668++67DmkhISEOBu24uDiXhv709HS+/vprIiMjbWk+Pj620LAAN910k603AiU35kZuuDur6rSInLL7nMZYx/E08EKRSlgIiN3Np7l6HnnkKypWfJ2zZ1OZOfNPm+F78ODrtNIoYnr06MHly5dtBleArVu35njAN2/enJYtW7Jq1Sqn9UyaNIkZM2bk2tbDDz/M77//zvffZ8VW//HHH9m9e7dDudtvv51z586xc+dOl23Zh3i1p2nTpkRHR9v2ExMTqVOnDl5eXixatMilIfzWW2/lyy+/tLmFP3v2LEePHuXChQtUrFiRwMBATp486dI4P378eJsh2/6TXWmAEdtjzZo1nDt3jnPnzrFmzZocAaIy+fnnn2nevDkhISG2tOTkZJvNZu3atfj4+NCyZUvAUIAnTpzIc3ZbScPdHseL2fYzgNPA70op5w79PUi11Av0iN3OxRQ9k6cw2bw5li5d5pOentV1b9hQ+/8qTkSEFStWMHr0aKZNm4a/vz9hYWHMnDkzR9lJkyZx7bXXOq2nVatWXHfddWzfvt1lWxUqVGDVqlWMHj2a0aONCQ5t27Zl1qxZOcbkJ02axD333OO0nj59+jgMR9lTsWJFGjVqxMGDB2ncuDHDhw/n/vvv54svvqB79+4OvQx7WrZsyeTJk7n99tvJyMjAYrEwe/ZsOnbsyLXXXkurVq245ppr6Ny5s8vzc5fq1avz0ksv2YbFXn75ZZuhfMiQITzzzDO2qcdLly7NYTQ/deoUvXr1wsvLi3r16jnYlyIjI+nYsaPT3lpJJs94HCLiAzwG/KCUOlEsUuWD0OBm6j/3f8CcTkZAm92P7+aN3kPoe2STrUzV/v2p82qEhyQsGzRr9l/27z9r2/f2FtaufZTu3ctXfBMdj6PwWbFiBZGRkS6N7GWZUaNG0bdvX9usq6KisONx5KnmlFLpZsCmUvNvCUg3xjQrdeuGf5vWVL33Xg9LVLqZOXOzg9Lo168ZK1YM8KBEmrLEvffeW+pmFRUWrVu3LnKlURS42z/aArQDnAfoLaFU7tWLqveWTH/2JZ34+ESCggKwWCyMHt2Rl176BS8vL2JiRurwrWWM3bt38+ijjzqk+fn58eeffxabDEOGDCm2tkoS+Vn4WJJwV3G8B7wlInUxXKxfss9USu0tbMEKhFKknzuHJSP3WMma3Ln99kWsXXuYunUrcezYWAAuXpzoYak0RUWbNm3YsWOHp8XQlCLcVRyZ8/Yy18ZnGkbE3PYuTKEKypgVGRyYdlPp8PNeAvnmmyjuv/8LMjKMn/f48SSsVmvZXPWt0WgKjLuKo1TYN5oeMx54ST7+XK5SjUbXX+dhiUoHVquVsLB3OX48yZbm6+tFZORQrTQ0Gk0OclUcIjIPGKWU2pdbuZJG+//9iKVWLU+LUSqYN287Tz650iHtmWeu54MPSra3Uo1G4zny6nE8DkyghHnA1RQet9wSatuuVasisbGjdC9Do9HkSl4rx107ZClhdIrKoHpS3uU0EB4+l6++Mnx7NWoUxKBB7fjyywc4cWKcVhqlABFxmAWVnp5OcHBwiYxpsXr1asLDw2nRogXNmzdn3LhxAERERBAQEGBb+Q1QqVIl27aIMHbsWNv+jBkziIiIcNrGN998w2uvvVY0J1AIKKUYOXIk/9/eucf3WP////40ynlo8VFCmTJm03KYEuZU+Bh9KFKofdAHU0J9xC+mfDrwDYmUUg6xKYeoPolqDinFyCEKMYdPizk0hzns8Pz9cb13eb+397b32N6zed1vt+u293Vdr+u6ntdr2/V8vw7X4+Hv709QUJDbly7PnDnjIrbo5+fHsGHDADh48CBt27YlKCiI1q1bc+TIEQASExN58MEHvXovGXgiOZLzG4LXALVu3Eyfb9Pt9RLlyudQ+vpl5syfKFFiPHFxCTz88GW9ow8+6Eb37g0KMTJDXihXrhw7d+7k/HlLGXr16tXceuuteTpHamrBzzzcuXMnkZGRfPTRR+zevZudO3dyxx132Pv9/Px444033B574403snTpUo4fz13deuLEiQwe7Lk5mDfu3Zkvv/ySvXv3snfvXmbNmsWgQYOylKlQoYKL9EmtWrVsP4+RI0fSt29ftm/fztixY3nhBUvl6eabb6Z69eps2LAhy/kKGk8Gx//MSQkSQFULdVbV3yv/h+/TbgGg5gez8SnvXqbgeiUpKZnatd/ir78ui72VLVvKzJi6ShrOLRhzsB39duRapmPHjnzxxRf06NHDVmPNUKg9efIkERER7N+/n7JlyzJr1iyCgoKIiorijz/+ID4+Hj8/P95//32eeOIJfv31VwICAoiPj2fGjBk0btw4W0+L2rVr069fPz777DNSUlL45JNPqFevntsYJ06cyJgxY+z9JUuWdHnAR0REMGfOHP7973/bEh4ZlCxZkoEDBzJlypQczaL27NnDjTfeiJ+fHwCfffYZEyZM4NKlS9x0000sWLCAatWqZbn3+fPnM2rUKNasWcPFixcZMmQITz31FGfPnqVr166cOnWKlJQUJkyYkK2UiqcsX76cvn37IiKEhoby119/kZCQQPXq1d2W37t3L8eOHbOVh3ft2mWLJIaFhdGt2+V307p168aCBQvyRVolL3jS4hgIPJLLcs1wg79/YYdwTfHwwx9TqdIkl6Qxfnwrzp4dbZJGEaZXr17ExMRw4cIFtm/fTrNmzex948aN4+6772b79u288sor9O3b194XFxfH8uXLWbhwIW+//TaVK1dm+/btvPjiiy6Krjl5Wvj5+bFlyxYGDRqUo1Bibn4e5cuXJyIigjfffNPt/iFDhrBgwQKSkrKXr9+wYQMhIZdnT7Zo0YKNGzeydetWevXq5SKu6Hzvs2fPxtfXl02bNrFp0ybee+89Dhw4QOnSpVm2bBlbtmwhNjaWESNGuJVVd7aJdV7mzZuXpWxe/Tyio6Pp2bOnLd0eHBzMkiVLAEue5cyZM/ab9o0bN85W0r4g8aTF8dm1KGRoyJ2XXlrD4sW77fU6dSqze/cgkzDyCU9aBgVFUFAQ8fHxREdH06lTJ5d93333nf2gadOmDSdOnLAfvuHh4baE93fffcczz1gab4GBgS6eFTl5WmR0odxzzz0sXbr0qu7j6aefplGjRi7jGRlUrFiRvn37Mm3atGxlxzP7eRw5coSePXuSkJDApUuXbP+OzPe+atUqtm/fbkvUJyUlsXfvXmrUqMHo0aNZt24dJUqU4H//+x9Hjx7lb3/7m8t1Fy1a5PE95tXPIyYmxkUI8f/+7/9s7/iWLVty66232qKIGV4e3ia3Fke+jm+IyIMi8puI7BORUW72DxeRXSKyXUS+EZFanpx3cDX3ypvXO2PHtkbEEiRct64f+/Y9bZJGMSI8PJyRI0dmUWPN6UGV2dPCHRmeFt988w3bt2+nc+fOLp4WGW59Pj4+OY4XNGjQwKUV445KlSrRu3fvbH23hw0bxuzZs21Z8sxk9vMYOnQokZGR7Nixg3fffddlX+Z7f+utt+wxhQMHDtChQwcWLFhAYmIicXFx/Pzzz1SrVs2tn0deWhw1atSwHf/ASm633HKL2/vZtm0bqampLi21W265haVLl7J161a7287X17IvKCwvD6/NqhIRH2AGln95feBREamfqdhWoLGqBgGLAfci/plYX7ZomaAUFC+9tAaR8bRsedmU8eLF0aSmjuX++2sXXmCGAiEiIoKxY8fSsKHrWEvLli1tk6I1a9bg5+dHxYoVsxzfokUL28xp165dts+Gp54WufHcc8/xyiuv2C5+6enpTJ48OUu54cOH8+6777pNQlWqVOGRRx5h9uzZbq8REBDAvn377PWkpCR7osDcuXOzje2BBx5g5syZtpfMnj17OHfuHElJSVStWpVSpUoRGxvLwYPu5fkWLVrk1s/DuVswg/DwcObNm4eqsnHjRnx9fbMd38jsHghw/Phx0tOtyT+vvvoqERGX3TX37NlDYGBgtvdZUOSYOFS1RD52UzUF9qnqflW9BMQALqNOqhqrqsmO1Y1ADQy5kpCQRLly/2HcOMvMZ/36y99uTAuj+FKjRg27q8mZqKgoNm/eTFBQEKNGjcr2ATp48GASExMJCgri9ddfJygoCF9fX4KDg21Pi4iIiCseeA0KCmLq1Kk8+uijBAQEEBgYSEJCQpZyfn5+PPTQQ1y8eNHteUaMGJHt7KqWLVuydetWu/UUFRXFww8/zP33328PmLujf//+1K9fn5CQEAIDA3nqqadITU3lscceY/PmzTRu3JgFCxZkO/CfFzp16sQdd9yBv78/AwYMcGldNWrUyKXsxx9/nCVxrFmzhrvuuos777yTo0ePMmbMGHtfbGxsvlgA55Vc/Tjy7UIiPYAHVbW/Y70P0ExVI7MpPx34U1WziPSLyECsQXtu87vzHt//u4F3p6VS+Rz4r1tLqevIX7xNm7nExsbb6yIwY0ZHBg1qWnhBFWOKkx9HWloaKSkplC5dmt9//522bduyZ88ebrjhhsIOLU8888wzdOnShXbt2hV2KF6nZcuWLF++nMqVK+dYzut+HPmIu24vt1lLRB4HGoN7vUJVnQXMAsvI6Z696VR23wVabPnii9/o0iUG57wfEvI34uKeKrygDEWK5ORkwsLCSElJQVWZOXNmkUsaYPmre1MC/lohMTGR4cOH55o0CgJvJo4jwG1O6zWALNMBRKQdMAZoparu266ZaPHL5aenT6Xrw8r0woVUO2lkCBIGBhp9LoPnVKhQgc2bN1/1eT788MMsU2rvu+8+ZsyYcdXn9oRq1aoRHh7ulWtdS9x8880u73R4E28mjk1AXRG5Hfgf0Avo7VxARO4G3sXq0srz2Er1CS9Togh+Y/KUyMj/8uyzTalTx4/u3RsQEvIdzZvfxvTpnXI/2GAoIJ588kmefPLJwg7D4EW8ljgcFrSRwFdY/h0fqOovIvISsFlVVwCTgPLAJ47pg4dU1eOvElII09K8wc8/J9C06fukpKQze/ZWzp+3BsdMt5TBYCgMvNniQFX/C/w307axTp+vaHTr7t+veTmtK+buu9/h55+P2uspKWmFGI3BYDB4JjlyzVP2kvWzRDFqccyY8SMlSox3SRrt299BaurYHI4yGAyGgserLY6CplyLFoUdQr7w7LMrmTr18iyRcuVKsXfvEKpX9y3EqAwGg8GiWLQ4AI7cRLEZGJ84sa39ecKEMM6eHW2ShsGmKPlxAHTt2pXmzZu73RccHJzlhTew9Jnq1atHYGAgwcHBbqU8wJIkWbduXb7Gm5+cPHmS9u3bU7duXdq3b8+pU6fclnv++edp0KABAQEBPP300/YLjXFxcTRs2BB/f3+X7SNHjuTbb7/12n1kpli1OIoqsbEHaN9+PkOHNmXKlAcpVaoUP/3UnyZN8uaxYPAuu+sVzIuAAb/uznG/sx9HmTJlrtiPI0MoryD566+/2LJlC+XLl+fAgQMuooO7d+8mPT2ddevWce7cOVtL6p133mH16tX89NNPVKxYkaSkJD799NMs5z558iQbN25k6tSpHsfjrfvO4LXXXqNt27aMGjWK1157jddee43XX3/dpcz333/Phg0bbAXiFi1asHbtWlq3bs2gQYOYNWsWoaGhdOrUiZUrV9KxY0eGDh3KgAEDaNOmjdfuxZli0+IoiqSkpHDHHW/Sps080tLUpXvKJA1DTmT4cUBWfaOTJ0/SrVs3goKCCA0NtR9IUVFRDBw4kA4dOtC3b1+Sk5N55JFHCAoKomfPnjRr1sx+r2PQoEE0btyYBg0aMG7cOPvctWvXZty4cYSEhNCwYUN+/fXXHONcsmQJXbp0sWXgnVm4cCF9+vShQ4cOrFixwt7+yiuv8Pbbb9v6Wr6+vvTr1y/LuRcvXuzigPfSSy/RpEkTAgMDGThwoP3tvHXr1owePZpWrVrx5ptvkpiYSPfu3WnSpAlNmjSxjZB++ukn7r33Xu6++27uvfdefvvtt1x+C7mzfPlyO/Z+/fq5TYAiwoULF7h06RIXL14kJSWFatWqkZCQwOnTp2nevDkiQt++fe3ja9WqxYkTJ/jzzz+vOsYrQlWL9HKb35266656OuHJAC1KjBnzjUKUy9K79+LCDsuQC7t27SrsELRcuXK6bds27d69u54/f16Dg4M1NjZWO3furKqqkZGRGhUVpaqq33zzjQYHB6uq6rhx4zQkJESTk5NVVXXSpEk6cOBAVVXdsWOH+vj46KZNm1RV9cSJE6qqmpqaqq1atdJt27apqmqtWrV02rRpqqo6Y8YM/ec//5ljrG3bttV169bpb7/9pg0bNnTZV7duXY2Pj9evvvpKu3Tpoqqqp0+f1kqVKnlUD3379tUVK1bY6xkxq6o+/vjj9r5WrVrpoEGD7H2PPvqorl+/XlVVDx48qPXq1VNV1aSkJE1JSVFV1dWrV+s//vGPLNc8ffq0BgcHu11++eWXLOV9fX1d1rO7txEjRqivr69WrFhRR48eraqqmzZt0rZt29pl1q1bZ/+OVVX79++vixd79sxw93eL9RrEFT13i0VX1WE/iGnlw5jcixY6hw4lUa/edM6fv6wEWqVKaQ4fHk7ZskaQ0OAZRcGP4+jRo+zbt48WLVogIpQsWZKdO3cSGBjIpk2buPnmm6lVqxY1atQgIiKCU6dO4ePjk6NXhTOZvThiY2OZOHEiycnJnDx5kgYNGtClSxfAkkHP4Ouvv2bXrl32+unTpzlz5gxJSUn069ePvXv3IiK2cq4zGRav+cm+ffvYvXu37SXevn171q1b51Yu3bluCsuLA8wYh9eZOXOTnTRE4P33uxAREZLLUQZDVjL8ONasWWM7wkH++XFs2rSJypUr88QTT1yRH8eiRYs4deqUPa5x+vRpYmJimDBhAtHR0fz666/Url3b3rdkyRL69+9PuXLl2L9/v4s/uTucvTguXLjA4MGD2bx5M7fddhtRUVHZenGkp6fzww8/ZHkwDx06lLCwMJYtW0Z8fDytW7fOcs0zZ87Ylq6ZWbhwIfXruzpFZHQ5Va9enYSEBKq6EWBdtmwZoaGhlC9fHrC6ITdu3EifPn3sZAJZfTwKy4sDzBiHV/jii9/sby+vvtqOKlVK07TpLaSnjzNJw3DFXOt+HNHR0axcuZL4+Hji4+OJi4sjJiaG9PR0PvnkE7Zv327vW758OdHR0QC88MILDBkyhNOnT9vxzJo1K8v5nb04MpKEn58fZ8+etZ393NGhQwemT59ur2e0IJy9PObMmeP22IwWh7slc9IAK7lnyNrPnTvXrX95zZo1Wbt2LampqaSkpLB27VoCAgKoXr06FSpUYOPGjagq8+bNczm+sLw4oBgljvtvdf8toDBJTk6hWrVJ/P3vMdSuPc3efuLEv/nxxwGFGJmhOHAt+3HEx8dz6NAhQkND7W233347FStWZNKkSdx6660uM8FatmzJrl27SEhIYNCgQYSFhdkD3a1ataJs2bJZrtG5c2fWrFkDWE6CAwYMoGHDhnTr1o0mTZpkG9u0adPs+qlfvz7vvPMOYE2JfeGFF7jvvvtIS8sfhYZRo0axevVq6taty+rVqxk1yjI+3bx5M/379wegR48e1KlTh4YNGxIcHExwcLDdxTZz5kz69++Pv78/derUoWPHjoA1sWbfvn00bnxFquhXjdf8OAqKmjffpbG3p1Hnp325F/YiAwas4P33t9rrIpCePi6HIwxFAePHcW3RokULPv/8cypdJ6rYGSxbtowtW7bw8ssve1S+KPtxXBc4CxJmUKNGRfbvd+tXZTAUGsXBj+ONN97g0KFD113iSE1NZcSIEYV2fZM48pHMUiElSggrVvSkc+e7CjEqg8E9xcGPo1mzZgV+jWuRhx9+uFCvbxJHPhIZ2dhOHA8+WIcvv3y8kCMyGAoe48dx/VFsBscLg+PHk6lU6TWWLPkFgDp1/JgwIYzExOdM0jAYDMUW0+K4Qrp0Wcjnn+8F4OGHF5Oe3gCAMWNaFmZYBoPBUOCYxJFHVq/+nQcfXEB6+uXZaPXq+RViRAaDweBdTOLwkJSUFPz9p3Po0Gl7W6lSJdiwIcIIEhoMhusKM8bhIf36LXdJGv36BXHp0osmaRi8jo+PD40aNaJBgwYEBwczefJk0tPTcz/QDWvWrLF9PFasWMFrr73mcfnMTJ06leTk5FyvuXXrVkSEr776Ksu+ZcuWISJZVHf37NlDp06d8Pf3JyAggEceeYSjR49mOT4hIeGa9SXJYO7cudStW5e6detm+3Jmz549adSoEY0aNaJ27do0atQIgEuXLvHkk0/aLwtmvAAJ0K5du2z9PvIb0+LIgaSkZHx9rTdWFy7swccf76Jy5dIcPPisESQ0MONfBWOkM+SdnD0WypQpY8tkHDt2jN69e5OUlMT48eOv6rrh4eGEh4df8fFTp07l8ccfd/uWtzPR0dG0aNGC6OhoHnjgAbf7YmJiiIqKAiw5kc6dOzN58mT7jerY2FgSExOpVq2ay/GTJ09mwADPVRnS0tLw8fHxuPzVcvLkScaPH8/mzZsREe655x7Cw8OpXLmyS7lFixbZn0eMGIGvr2Xk9t577wGwY8cOjh07RseOHdm0aRMlSpSgT58+vP3224wZU/Byr6bFkQ333TebSpUm0arVh/a21NSxJCY+b5KG4ZqhatWqzJo1i+nTp6OqxMfHc//99xMSEkJISAjff/89AH369GH58uX2cY899piLBwZY+kyRkdaLqr///juhoaE0adKEsWPH2gJ8AGfPnqVHjx7Uq1ePxx57DFVl2rRp/PHHH4SFhREWFpZtvKrK4sWLmTNnDqtWrXIRIjx79iwbNmxg9uzZLt4dCxcupHnz5nbSAAgLC3Or07RkyRLboyO7ulizZg1hYWH07t3b1vn66KOPaNq0KY0aNeKpp56yJUey8yW5Ur766ivat29PlSpVqFy5Mu3bt2flypXZlldVPv74Y9tvZdeuXbRtazmEVq1alUqVKtnv4oSHh9t6XwXOleqxXyvLbX536r4mdTzSpPeEOXO2qIirT4bBkMG14seRmUqVKumff/6p586d0/Pnz6uq6p49e/See+5RVdU1a9Zo165dVVX1r7/+0tq1a2tKSoqLj8eHH36oQ4YMUVXVzp0768KFC1VVdebMmfY1Y2NjtWLFinr48GFNS0vT0NBQ29uiVq1ampiYmGPs69ev1zZt2qiq5YuxZMkSe9/8+fM1IiJCVVWbN2+ucXFxqqr67LPP6tSpU3Otl/3792tISIi9nl1dxMbGatmyZXX//v2qav1O//73v+ulS5dUVXXQoEE6d+5cVc3el8SZiRMnuvXnGDp0aJaykyZN0pdfftlef+mll3TSpEnZ3tPatWvtuFVV3333Xe3Ro4empKTo/v371dfX18WTw9/fX48fP57lPMaPww0eyvfnSHJyCjVrTuHEifP2ttKlS7Jz51NXf3KDoYBRh+ZcSkoKkZGR/Pzzz/j4+LBnzx4AWrVqxZAhQzh27BhLly6le/fuOVqo/vDDD7bbXO/evRk5cqS9r2nTptSoUQOARo0aER8fT4sWLTyKMzo6ml69egHQq1cv5s+fb/t7REdHM2zYMHtfdHQ0ISGeq0dn9ufIri4y7iFD7v2bb74hLi7OFkY8f/68LX+eky9JBs899xzPPfecRzFm/J6cycl/JLO7Y0REBLt376Zx48bUqlWLe++91+X3mOHRcdNNN3kUz5VSPBJH+ayS0XkhKupbxo9f77Lt+eeb8/rrHa7qvAaDN9i/fz8+Pj5UrVqV8ePHU61aNbZt20Z6ejqlS5e2y/Xp04cFCxYQExPDBx98cMXXy/DjgNw9OZxJS0tjyZIlrFixgv/85z+oKidOnODMmTNcunSJb7/9lp07dyIipKWlISJMnDiRBg0asHbt2lzP7+zPATBlypRs6yKzL0m/fv149dVXXc6Xmy9JBpMmTbJl7J1p2bIl06ZNc9lWo0YNlwHtI0eOuPX9AEuPaunSpcTFxdnbSpYsyZQpU+z1e++9l7p169rr3vLoKBZjHD61r87TomHDywNsNWv6cunSaJM0DEWCxMRE/vWvfxEZGYmIkJSURPXq1SlRogTz5893kQd/4oknmDp1KgANGjTI8byhoaG2i2Bmr/DsqFChAmfOnMl2/9dff01wcDCHDx8mPj6egwcP0r17dz799FMWL15M3759OXjwIPHx8Rw+fJjbb7+d7777jt69e/P999/bHusAK1eutP1DMrjzzjuJj4+313OqC2fatm3L4sWLOXbsGGANYB88eNBjX5LnnnvOrT9H5qQB8MADD7Bq1SpOnTrFqVOnWLVqVZYJAs71Va9ePbt1B5Yw5blz5wBYvXo1JUuWtH1AVJU///zTNscqSIpF4ijhNHDnKSEh7/D778cB6N69AR063MGqVY9z8OAwSpUyg9+Ga5fz58/b03HbtWtHhw4d7IHbwYMHM3fuXEJDQ9mzZ4/LN+tq1aoREBDgka7U1KlTmTx5Mk2bNiUhIcGe1ZMTAwcOpGPHjtkOjkdHR/PQQw+5bOvevTsLFy7McV+ZMmX4/PPPeeutt6hbty7169dnzpw5Wdz0ypUrR506dWxzp5zqwpn69eszYcIEOnToQFBQEO3btychISHEDimQAAAMsUlEQVRffEkyU6VKFV588UWaNGliTzyoUqUKAP3793cRnYyJiXHppgJrFl1ISAgBAQG8/vrrzJ8/394XFxdHaGhojl2Q+UWx8OPY+NTD3DJhgkflJ03awPPPfw1YYxjnzxcFp3LDtUJR9uNITk6mYcOGbNmyJddEkJycTJkyZRARYmJiiI6OdpmVda2ybNky4uLimODh86A48cwzzxAeHm7PunLG+HG4wad8hVzLHD+ezO23v8nZs5fsbaVLF4vbNxhy5euvvyYiIoLhw4d71HqIi4sjMjISVaVSpUpXNSbiTR566CEX//XricDAQLdJoyAoFi2OuHFPc3PkkGzLdOz4EStX/u6ybcqUBxg2LDSbIwwG9xTlFoc3adasGRcvXnTZNn/+/Cz+6AbvYFocbvCpkP0YR+fOC1ySRoMGN7Nz52BvhGUopqhqjlMoDfDjjz/mXsjgFQqicVBMBsez76r69NNHAEuQcOvWgSZpGK6K0qVLc+LEiQL5ZzQY8puMKc/OU5Hzg2LR4nCeVTVy5Fe88cZGhg1rxpQpD1KqVCnOnRttZEIM+UKNGjU4cuQIiYmJhR2KweARpUuXdpnSmx8UizGO3cs/5M9qdxIY+A4XLlyeq6169doyBoPBUBy5mjEOr3ZViciDIvKbiOwTkVFu9t8oIosc+38UkdqenLfr4O/x959hJw0RiIn5R77GbjAYDAYLr3VViYgPMANoDxwBNonIClXd5VTsn8ApVfUXkV7A60DPnM574swFvtl2zl5v2bIma9fm/oKTwWAwGK4Mb45xNAX2qep+ABGJAboCzomjKxDl+LwYmC4iojn0pyVfTAWUMmVK8euvkdSsmfscdYPBYDBcOd5MHLcCh53WjwDNsiujqqkikgTcBBx3LiQiA4GBjtWLMH7n+fNQq9b/K5DAixB+ZKqr6xhTF5cxdXEZUxeXuetKD/Rm4nA38T1zS8KTMqjqLGAWgIhsvtIBnuKGqYvLmLq4jKmLy5i6uIyIbM69lHu8OTh+BLjNab0G8Ed2ZUSkJOALnPRKdAaDwWDwCG8mjk1AXRG5XURuAHoBKzKVWQH0c3zuAXyb0/iGwWAwGLyP17qqHGMWkcBXgA/wgar+IiIvYVkYrgBmA/NFZB9WS6OXB6eeVWBBFz1MXVzG1MVlTF1cxtTFZa64Lor8C4AGg8Fg8C7FQqvKYDAYDN7DJA6DwWAw5IkikzgKSq6kKOJBXQwXkV0isl1EvhGRWoURpzfIrS6cyvUQERWRYjsV05O6EJFHHH8bv4jIQm/H6C08+B+pKSKxIrLV8X/SqTDiLGhE5AMROSYiO7PZLyIyzVFP20UkxKMTq+o1v2ANpv8O3AHcAGwD6mcqMxh4x/G5F7CosOMuxLoIA8o6Pg+6nuvCUa4CsA7YCDQu7LgL8e+iLrAVqOxYr1rYcRdiXcwCBjk+1wfiCzvuAqqLlkAIsDOb/Z2AL7HeoQsFfvTkvEWlxWHLlajqJSBDrsSZrsBcx+fFQFspnm47udaFqsaqarJjdSPWOzPFEU/+LgBeBiYCF7wZnJfxpC4GADNU9RSAqh7zcozewpO6UKCi47MvWd8pKxao6jpyfheuKzBPLTYClUSkem7nLSqJw51cya3ZlVHVVCBDrqS44UldOPNPrG8UxZFc60JE7gZuU9XPvRlYIeDJ38WdwJ0iskFENorIg16Lzrt4UhdRwOMicgT4LzDUO6Fdc+T1eQIUHSOnfJMrKQZ4fJ8i8jjQGGhVoBEVHjnWhYiUAKYAT3groELEk7+LkljdVa2xWqHrRSRQVf8q4Ni8jSd18SgwR1XfEJHmWO+PBapqesGHd01xRc/NotLiMHIll/GkLhCRdsAYIFxVL3opNm+TW11UAAKBNSISj9WHu6KYDpB7+j+yXFVTVPUA8BtWIilueFIX/wQ+BlDVH4DSWAKI1xsePU8yU1QSh5EruUyudeHonnkXK2kU135syKUuVDVJVf1Utbaq1sYa7wlX1SsWd7uG8eR/5FOsiROIiB9W19V+r0bpHTypi0NAWwARCcBKHNejH/AKoK9jdlUokKSqCbkdVCS6qrTg5EqKHB7WxSSgPPCJY37AIVUNL7SgCwgP6+K6wMO6+AroICK7gDTgOVU9UXhRFwwe1sUI4D0ReRara+aJ4vhFU0Sisbom/RzjOeOAUgCq+g7W+E4nYB+QDHjkgmckRwwGg8GQJ4pKV5XBYDAYrhFM4jAYDAZDnjCJw2AwGAx5wiQOg8FgMOQJkzgMBoPBkCdM4jBck4hISYeabbfCjuVKERF/xz00yqXcRyLyqbfiMhiuFpM4DAWCiMxxPDQzLzk+RL2JiExwiitNRA6JyCwRyS+NswNAdWCn43rtHNeqlKncEApYFsXp2hnLCYfkfmgez1PkE7rh6jGJw1CQfI314HRe3PoCFCK/YMVVE4gEHgLm5MeJVTVNVf90iG7mVC7Ji3pRd2HdbxhwCvjS8Ra5weAxJnEYCpKLjgen85IKICKdROQ7EflLRE6KyJcicld2J3JIIkSJyEERuSgiCSLyodP+EiLygojsF5HzIrJDRB71IMZUR1z/c7xRPB3oKCI3Os4bLCLfOs55QixjnAw5buf9p0XkjIj8LCKtHPvsrioR8QdWOw475dj+vqOc3VUlIkNE5A+HQKPz/X8sIkuc1ruKyBYRuSAiB0TkZYe8Rm4cc9zvduA/QCWgidN5m4nIahE5LiJJIrJeRJo6HR/v+LnMcQ/78iEmQxHDJA5DYVEOmIz10ArDkjv4TERKZVP+EWAY8C8sYb5wLE2iDF4F+mIZV9UHXgdmS96lw89j/V/4iEh5LNmKU1geD92xjHHecyofgyVL3RS4G3gJ974fBxz3AJe/9Q93Uy4GS2yvTcYGR6LqAnzkWO8EzAOmAQ2wBPt6Oa7tESJSjsvdYylOuypg+drcjyUKuQOrVVLZsT8jyTzpuIfQ/IrJUIQobIcqsxTPBau7JxU467R8mUP5ikA6EOpYL4mlIdTNsf48sAso6ebYClgP6+aZtk8HVuRwzQnAz07rAVjOcRsc64OwdM/KOZVp54jrdsf6OeCxbM7v7yjbKNOxlTKV+wj41Gn9M+BDp/UnHHHc4Fj/Hngh0zl6YAnUZXevGdfO+F2oY/nRXZ06HSdY4n+93P1enMrlOSazFN3FtDgMBck6oJHT0j9jh4jUFZFoR9fSaSwpZ8Eaa3DHIqwEcUBE3hfLQzyjGyQQuBFYLSJnMxYsx7s6ucTY0FH+PNZ4RzzQx7EvANimquecym9w2gdWq2mOiHwtIqNF5M5crucJHwH/EJHSjvXHgE/UcrMDuAcYm+le5wEVReTmXM59P5aV6KNYraC+6jQGIyLVHBME9ohIEnAGyxAtu99LBlcTk6GIUSTUcQ1FlmRV3ZfNvi+wHlwDsJJGOlaLwm2fuKoedDyU22HJYU8BXhTLhCfjC1Bn4H+ZDr1EzvyG1e2VBvyhrt4lQlZTG3X+qaovish8LIXRDkCUiAxQ1blcOcuxPLG7iMh3WF15zl0+gqVyutTNsbl50BxQayB+j6O7apmIBKtqRnfVR1jjHsOAg8BFYA3Z/F7yKSZDEcMkDoPXEZFqWOMU/1TV9Y5tTcllzE1Vz2N143wmIpOwTGhCgTisBFFTVdfmMZxLOSS3XcBjIlLOqdXRwvFzt1Nce4A9wFQReQ+rf99d4shIYj45BaSqF0RkKVZLowbWfX7nVGQrcFcOcXvKHOBFrC65aY5tLYCBqvpfALH8p//mdEyaY8l8D/kVk6EIYBKHoTA4jvUtdKCIJGA9HCdhtTrcIiIRjo8/YY0r9MYa1N2nqkkiMgWYIiI+wHqsMZPmWInh/SuMcz7Wt+i5IhKFNWg9E/hYVeMdg+evAouxuriqA/dhddG546DjZ2cR+RI4r6pnsyn7EZZXwl3AAlV1bvmMB5aLyGHgE6wHeUPgHlUd5enNqWqaiLwJvCAi76tqMlYC7CMim7G6BidhtToyjlEROQS0FZENWDPnTuVXTIaigRnjMHgdVU0DemL1te8E3gJewHV2T2b+AgZiffPeAXTFGqA95Nj/AtZg97+xWgOrgG5Y3WFXGudZ4AGgCtYMrqVYSWmAo0gqVjKZh/XAzdg/MpvzHcR6wE4EjgJTc7h8LHAMqIdjNpXTef6LNcuqvSOun7AmDxwi77wPlMF6hwWsgfhKWC2IhVhOkoczHTPcce3Djuvnd0yGaxxj5GQwGAyGPGFaHAaDwWDIEyZxGAwGgyFPmMRhMBgMhjxhEofBYDAY8oRJHAaDwWDIEyZxGAwGgyFPmMRhMBgMhjxhEofBYDAY8sT/BzHRTybgvEkOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UJyiD0ZqxMdUddnDPb5ddqn92/n+5enW1cvQCtOOQFpHtNPsZIg91OkYrM3p06pBut7+0SPOEPytWwAUXtC1K4jXmzm09LWgdef55mD7dLQdft4GBaJ9E1HPthCjH+YwZZmLKwsEHwy9+4Z7jwQdXK4v/va5eDYceWq0s3U67Duk3FSRPzxA1c1rWSK2teikFbbtpJuOpG0ND8fcXNydG3vbs665r3hYVCsWIJ+kdLZOgyfH44+Hss+0/LIpY5aCq/1WmIFUyNNRejfzZZ5un38zqFxgcdD2V/DAVQcK23W6bYAjcM/rmN6P3jY7C+9/vWhY+Rdizn3suerv1uMlOlaOSfR+Vz6OPRgfDNPLBHNLAu9/d3nmbNzePts3ao+i559wHNzTUfH7Y7NGNEwxt3uyc91GMjLian09RYTd23DF6u/W46S5sxHu5pFYOIvIXIvJ5EVktIleG0i+KFLJovv3t9s8N1z6DNZu0bN7sflUnRjoNF5J+18y4sBXdgMjELqtHHeV+Z8+eGHYjzy6ur3518zbrcZONOpiVbMxFuaRSDiLyGmANcCjwVmA2sCfwRlxE1q4OgdWJkzdc+zz/fDjooPZkSFMAjozAHntkz79OtOqy2k4X1yRlEp7kqC6BAY1s2JiLcknbcvgUcDnwcpwiOElVFwEHA5OAykJnVElwDEKQn//cRWn1TUVpSds89gMFTpuWLf864ZsDomqkWc0HrZRJ8BrveU9zYECjNXWIgDo62uj15mMtwOJIqxz+Chij0bV1EoCqXolTDJ/OX7TyyFqI+8ycGV/IjIzA449na46naR6Pj8Pdd7vlblYO4O436vlkNR+0UibBa9TBPNKN1OG5jYzApwMlzS67WAuwSNIqhynABlXdDjwB7BrYdwcuAF/XsmpVe+fl3bW0VfPYryH7folnnsn3+mUzZw585ztu+cknG+aguOcwMBBtWmqlTPIcM9HvVN2COOKIxvKll5piKJK0yuFuYDdv+VbgRBEZEJEB4ATg4SKEK4t2X7A8bZ1pmsdRNeRu5oUX4B//sbHum4OWLInulbVtW7TvoZUtug613m6nLs8wKMeVV9o8E0WSVjn8B875DM7/cCjwDPAk8D7cnA5dSzsvlUh+ts60DtJe65WxYcPEMQ7glN/q1fGDCaN8D6OjMGXKxG1BZVuXgq0XqLrlEGTVqmLjcvU7aaOynqmqy7zlnwP740J1/ztwqKp27TSh4YE1aVFN1+JI87KOjqbLq196Zaxbl+ycj5rH+8gjG+thZWvKoTfZtGniuo15yJe2BsGp6m9U9Z9U9UOq+tO8hSqTdk01acN8n3JK62PS1ni6cRBcuySNBI9Skou96DAf/nBzb6Sgz8EURW/Ta63rKkk7zuFyETlCRKa0Prq7aPdlSmNSGh+PD90QJG2Nxx8El1Yx9SJxvhm/e2+U89kUQv/QL63rMkjbcvhz3DiHh0TkPBHZv0CZSqXdlymNGShLEzdtzKSREVczHhuLbkW02y23TNqVMck3Y8qhWOr4DMNduW3MQ76k9TnsA7wKN9bhSOBaEblLRD4mInsWKWDRFGmqydoqWbEi/bHBVoTvJBTpjsit7Y5ITxq85sekCgc+HB+fGB7lBz8wp2Un1MkhfdppjWUb9Z4/qX0Oqnqjqp4GLADeDtwA/G/gLhG5piD5CqdIU03WVknWcN9+K8I3XQ0M9EezOipURpRyGB93s8AFI976gQBNQXQ/wTA1Nuo9fzI7pFV1m6quVtX34VoRDwKvy12yEhkZKaY5GtXFMolt2yYWWmn7bg8E/sVed1rHhcrw53oKmpVWrowOhR4VTddIpi5mpbrI0Q9kVg4i8mci8nERuRP4MS7W0udyl6xkiigsRkbg4ovdnA1pEJnYrTZt3+1vfcv9btvm7mPp0nQtIb+2XSdTgc/y5c3bBgbcvUWFyvjBD9xyUDkkmfXy7NWSZwTZulPHd8UoiLj5Q4MJF4X1FOBaYBvwLHAp8DfgphqtMnU6h7Rq9rmRsxA3V3LUfMtZ51QeG1OdMWPi8TNmNOa0jrtWq3mcq0wiTr52zh0cTHdvec1TnfT8e4kDD3T3dtVV1crx+c83nvMuu7T3PRoNSJhDOlXhC2wCtgI/BY4FZqQ5r6zUqXIYG4svmFsVrmnImndUYRlHXAE4POxknDSped+0aQ35O5WtjklEdflyd49TpjTvnzo1v8I76fn3AuFKxsqV1coyfXr08zbaIw/l8BFg1zTHVpE6VQ7t1J6zfPyd1s6TrhVXuPu174GB6P1pWhfdnPz7DyvIPBVDq+ff7US1ioIVi7JJeleN9uhYOeSZgENwkVzXAqcnHPcuQIHFrfLsVDm0U3vO8vG3ayLxr+MriKiPMqnm2qrg9/MMFwC9kvz7mzatsW3x4o5elUzPv9up270lfadGe9RGOeDmgbgbN4vcVOAWYJ+I42YCVwPXl6EcOmk5LF/eqJlOmuTWo8ijsIuyZSfZvFspPV/BjY2pDg0VX1hXkcLPZtKkfGu+UaaOXvE51K1VZC2H/KmTcngt8JPA+keBj0Yc9wXgb4GrylAO7dTsx8acIojaF1YQnbQcwimq1uabh0QmtjDStByi8qm6QM8rRflbilAQ55478Zn2gmJQjX8Xhoaqkcd8DvlTJ+XwLuArgfVjgXNDx+wLfNdbjlUOwDLcvNZrFi5c2PFDympaUk0ufHzinMLtpqzmrDiTUVLttupCPY/UylSWZ+3+4Yddnrvsku2/iVLodaIMh35WrLdSvtRJORwVoRzOCawPeAphkbdeSstBNb4VEJdatQZ88jbXZLX3BlsDvpJqVRhVXbBnTUGfQvD+srac2uWRR1x+c+emO76bur/Gvb9V+R3uvrshQ1A51PHZdQN1Ug6JZiVgZ+Bx4F4vvYAbgZ2oIPJQDqqqO+6YvkCaMSO+J1Cw5dBJoTd16sR1v4tm0VRd2GdNM2c2lo8+unEfaZztnRYqY2OqCxa4vAYG0uVXN0dvEnXzOwSVQzco17qTR1fWw4ETAuvDwHXeYLjLgB1T5jMZuAfYI+CQfnnC8aW1HFSz+wYGB6O3BwvwTgq9qA+z6I8gT/9IWSn4nILKQbV1i7CT59luC6BuBW4SdVNkccqhrsq17uShHG4A/ldg/bvAfbiwGY8C/5omH+/cJcCdXq+lld62TwCHRRxbqnJQzV4oBQufqN5KRfQCChYuQ0P5KYtu7dY6d25j+T3vyX4/7TpY2y0461bgJhFVWaiylp6kHOqoXOtOHsrhCeAQb3k68DxwlLf+d8DdafIpKlWlHPyP2V/fsKE5vzinXp4pLwdht/ZUCpqVXvOa9u6nnefXbgsg6p2os1kkKOeLXlStnNZyyJck5ZA28N4OnkIAF4F1Mi6UBrgBbfNT5tMziMCSJRO3qTYf5wff84PcFUFeUUa7dYrFZ59tLK9Z0wh8l3YCJWjv+cWFR28VNn1kBN73vsZ6N81FcNll9ZTTJvrJn7TK4V7gAG/5cOBGVX3aW98FeDrqpG4kbQRVVbjkkokROKOUA7iP6ZJLig2lnUfB3gtzQfiRacfHs0UQXbcue1TVqPDoaQupV73K/S5fbnMR5EG3KNduIq1y+DfgTBFZA6wA/j2w77XA7XkLVgXj47BhQ/rjw3M/xykHaG9SofBcEEmtD79g7yR8dK/MBfGnP7n/Jen/iEI1fZh0aPynQSWUtpDKorj6KSR4u5hiKIA4e1M4ASPAOcBxoe3/BhybNp8iUl4+h05t7k8/nf5aBx2ULe/jjms9KCmP/vO9Nkq63ZTFfh0cZ5F2UNt557njTzml9f9R9ZiI4LWvuaa860YR53Mw2oO6jHMoKuWlHNoNwOcvP/VUtuvNnp3+Ogcc4M4J9x4J9lbKsxdMN3ZpzTNl6fkSNd6lVQF+/vnuuJNPTs67Dj2b6qQc1q6Nfh5GeyQph1RmJRF5iYi8OrA+XUQ+LSL/ISIfyL05UxHt2NxVo5fTkMWBdu21zqwQdpw+/nijSR3nd2jHHzEyEj0bW5BenhWslZkuuD04+5xP2OQYJu2zy/M/NYxMxGmNYML1TPpsYP1sYDNwI7AF+Ps0+RSV8hwE18nkN088ke16jz/eeQ03SBG1zHDE1mBLZWwsfiBgt6Q5c5pHovu1/jiTzvLl6cZPRLU+wma7N70p+fnXreVw9dXlXTcKaznkCzmMc3gYONxbHsCFuPgHb/3jwC1p8ikq5TnOYfny9hXEggXu/LQB1Z54ovPCbWioca2oQqsM+3QwbLlId80ut2CB6oknNtbTRLVNG0gxPLguStlMnpz8/9TN5+DfV1VjHc4+O/pZ13WMSN3JQzm8ABzgLb8SN4/07t76G4Bn0+RTVMpTOaiqnnBCfoVP0of81FP5FnR+rdYPazx3bvkfTTc6s/3nteOOE++lUyUXVg7ttgKC/p+yI7jG+Z6qisw6f357z9CIJg/lsA443lv+KHBnYN/bgCfT5FNUylM5FDGiOe7FfeaZ/Au64WHVd7zDLV92WW6PJTXd1GoIp5kzJ95Lp4oubFbqJKaSf2zZJD2DKgrkbopL1Q0kKYe04xyuAD4tIv8KfBj4TmDfX+KC6fUEK1fCli355hnnPLzssnyvE75WFQ7jugykyzKexCf8vDod9xF+Fu2OqK6SJMd3FU7xXXeN3l7nZ9itpFUOpwM/BN6KUxSfCuw7jEYoja6niBd+zpzo7R//eDHXuu8+t/yud5U/aKouIQz22iv7OWHl4A9y88kSAmXq1OZnEadsnnuuvgPbkgrdKgrkD30oentd3rueIq5J0U0pT7NSETbzuKifRZhgBgebHaZVOzC7Jc2a1biH4ExtQdNFmnwGBuLn3Yiz4bf6j6oyK8XJm/dUq2m5885oeYz2IAezEgAiMkdE3iYix4rIEhGJqRN3L+FgennwxBPR2+NaFJ2wYYOLLxRk40Y49dT8rxVHOyadOvDUU66ltWKFC6Gxbp0renzS/l/btzfH3fKJC/NQ9n+Uljh5iwwkmUTw/zCKJbVyEJFPAg8A/wFcgjMzPSAi/1KQbJWwenX+eUY1v8fH4Zln8r9WHOvXl2e66OYm/rp18KUvucI6iqlT0+XTahBcFGX+R52yeTMcc0x9Yj2tWFG1BD1IXJMimIDTgO3Al3FdV1/q/X4Z1631g2nyKSrlaVbK29QTZy4ownw1ZUry5EJl9i4pYpKjOqQsg/7ietAknRP3H5VtPokyq2V9x4vgD3+Il6OMKXR7DXLoyvoH4PMx+z4P/CFNPkWluvocFiyI/2iK8DdMnZo8LWaZ3f26dVa5PFOcrynpnFYKpQza+e/KqngkKYfg3O1GOpKUQ1qz0iLgRzH7fuTt7wnKMokU0dNj82ZnFguH+i7ymnEEQ5SLdK8fomziYjq1SzvhvleujDerxVFWt1bV+H1hX5vRIXFaI5hwvobTYvadCjyQJp+iUt4jpHfYIb/aY1yTu6iatYjqfvull6NMunH0dKf/RRSt3pWkdyML7YTeaDcab7sth6D5Ks3o789+Nl4GazlkhxzMSucCzwLHAlO8bZOB9+JmgftimnyKSnmPkM67kIj7cDqJ45T0gURdPxgsL8vHmCf9ZmoaGmp+1nHvV5rQ65DteWcN19Hu/5NUAUp619pRXrvuGi/HQQfFn1fle19n8lAOM4FrcE7pLcAj3u924GpgxzT5FJXq6nPwk1+DDL+gZTltfeoQxK1fJhOaNCk62mvcfz4w0PgfkioMWcgaaqKd/2XhwvQt4/C71k6sqaRnk0Xp1aElXQc6Vg4uDwT4W+AsXC+ls4AlgKTNo6hU595K/ktbZa3Zpw7hn32qeA5lpqgJgFolP5hdVS2Hdt79bdvav3Y7cZKSWg5ZlZ4F61PNRTnUOdW55eDXUKqsLfstlVYfVZlN76qeRd2Tb16qwueQ9h0NFupbtkTnlabgb6fQ/sxn4uXKqvQsWJ+qKYcM5OlzEGn0va5DtNI4GeJaNkU2vat+FkWntHM+pPmvgqaorGQJ993OZFebN0fnlabgHxuLn2gpjttui863HaWXR8uh230ZbSkHz5+wLWXaGpdPGSnv3kp5FhL+Cxj3gpY9WCz88bdq2RTV9C7znqtI++yTX15BpagX60QAABdiSURBVN3Js87rfwm+Q5s2ReeTtrLxwQ9OfNdaFa6//W20TFVMmNQLvox2lcOZuFneUqW4fMpIdVYO/oub9CKVXXDttJP7nTWrtRO0iKZ3FffcK6mdWqp/bpr/JU3LIViReOGFdP9znLyXXeb2v+Md6e4lTjmkubdWsmSlzApVUS0UMytlJO8POth/fccd3bbZsxt/cJmth0mTVE8/3S1/6lONey7qRY96qeOuFTYxWEr3XrV6/sH/Mun4tD4Hv3IBqs8/H5/fpZe2vvZ3v+v2RymHqHfn05+OlilNYekfmxdlVaiKbKGYcshIEYW1X8iecopbP++8Yq+XlKKUw9hYY7rMvF7AuJc6Ti6RfM0xUWlwMFt8pLqnJOWdtVBpxy+2cWNn1/aVwxFHpDt/1qzsz8HHPzYtrWrrZbUciryOKYeMFGH28GsTJ5/s1s8/v3G9Mp3Vw8PRykFV9YILJh7Xac0k7qWOc9ZOmlSsopwypXFPZT3volNSLTXuWcYVKu20HDZsyJZX+NqXX+62h5VD1t59eU+1mka5leVzKLKFYsqhDfIupPyPYtkyt37BBdV0cQ3e11FHTbznRx912+fOzecZ1qGHVtz/0M44hDqmpIFfceeEC5Xge5jmP3vb2xrLzz6b7b8PXztOOeT1HKLyTGO3T6vcivBlhMmq5LOQpBwyTfbTT6xalW9+/iRCqu73v/+7MaFMWQwMuDkDfL7zHZg718XCX7QIXvQit33TpnyuFxfor4q5rX38AHFHHFGdDHkxY0Z8oMikuSSC/8v4+MT30H8/k3jZyxrLccdnnS87nE/WyYRaBcwMBhxUdfe7bFl8IMK4QILh7cHJkO69N35ypDREBUkcH4dnn20+dsqUEoKExmmNbkpFtBzyNi35Wv6Nb8w336JSHjWgqGZ3O60JEdVp0/L7H8bGVF/0ouqfcd7/Udo5GIJxtjodj7H77tE18bQ+rO99z+077LCJ27PK0Yqsdvssx6eVIYk4E1VcqyEuHHxWMLNSdoow9yxf3vnH2CrlZS7Jy6k2NtYo2NMWyFEB6/Iy8w0O9kavqPAczmnDs/iFShHhXIK98sLfT5zJ5fvfd/vf/vZ0319c+Iw4BdVKWcbZ7Vv5E6Ly7oR2yhvrrZQiFaEcirCXl2GDP/LI/GTNiwMOcHlefXXrjyA4qjzt/zFzZusQIb2Y/Jn/RNJVOqZPTxf5tZM0NBStdOIKsjjlMDbm7i98v5/8ZPL104Q9D6ZWvb2CxwUVQ5Z7TEM7ZUMezu8k5WA+hxiKmBhHNf88w/z1X+eTT573H7zv0VFnK0869pJLmm3BSfJ84xuwfbuz+fYTW7Y4H5JquolujjyyYRMvanKe9eujJwrKOp82NPum0viq/Lm7005Y5PsCo4jzJ8TlvXRp+/Npt/O9tTNPeRZMOcQwOlqu4zSrAy6OW2/tPA+RYpxdIo0Z4oaG4o+LeumTlMr27e63DhPd15nLL288ozJnBYR4ZeR/Y8EKxPi4K2g3b5547ObNcO656a6VVvmtXp3uuHD+UWzbluzkTiJJSbUjSy7ENSmKSsAhwB3AWuD0iP0fAm4HbgV+AQy3yrMIs5Kq6i67FNP0jkp5mZzCTsB20qxZ+T7H17/e5XvNNW49TZM/yqwVtPPuvPPE43fcsXe6pxaZfDNKO0H2WqUkB2o44J7/P86b5/a/7W3p340095glwmwS/nFBWuXdjr+uXTNfp75B6uJzACYBdwN7AlOBW4B9Qse8CZjhLS8HvtUq36KUw9575/vxwETbcPCFqNpeHpRr992bn0UnsV1e9zqXr68c0txrK1vw5MnVPq920sKFqvPnVy+HHxY873tLsvW3ste/4hXp3o1WFYCgzyFNRalV4eofF37/Wo30z0odfQ6RG4tKwGuBnwTWPwp8NOH4fYFrW+VblHJ46Uvz/YCiXqLgx1PlFJqvfW1jeeHCic+h3ZGg4V4rZ5zhtrf6EOKc0j6dKNKie4slpU2b6qEcwPXYynOgZzAAX5Ti8SsWcc9/+vTW70aaAjT43pxzTmN71L0GHfpxFR7/2Kh3O06GsloOSd9IWuqkHN4FfCWwfixwbsLx5wL/FLNvGbAGWLMwXJrlRBEfclw3yrgugGWk+fNVjz66+QVv1bMla1yfadPS32OSgujEHCKiuttu5T9jUL344nqNGi8iCkAn4UmS3rUs9+Rz//1u2/z5qtu3Nx+XZi6JoGxhoq7fbm1+bCx7F+ueiq0EHBWhHM6JOfYY4HpgWqt8ixoEV4RNNm4wV/CPLltBfO5zqvvvH/+itxPbJUmhpDVpiER/aJ08n+Fh1WOPLff59lPqJAy9SHShnTX570xQOWzd2vwexL0f/vcfVJ6+KS5I1LmdmHkOPDD78+qUOimHVGYl4GDg98AuafLthkFwQ0OqBx2U7o9evjzfa7dKd90Vb9JKcu4l1VySFMrYWHrncdQ1OrGXj41V13KwlC5NmdLZiHj/nQkqhy1bJh7T6v0Mj7GAxhzfPuH9vsksjZII+/CWL7eWw2TgHmCPgEP65aFj9vWc1nunzbdbBsGl/aPLbjncdVf8vriPpVXzOe4eRLJ9BHG1o6Rz/DkzolKrcy3VI3XSevDfmaBy2Lx54jFxvo9WPZ2C32mSDFERXH1lMDTU/D1lLW98E22n1EY5OFlYAtzpKYCV3rZPAId5yz8HHgFu9tIVrfIsQjkkFTBFpOAfXbZiuvPO+J4d/sewdGn0/qjmtmp87StriqsdtSoc4vapWsuh6jR1arHzdkS1HL761dbntTKj+intaPygiSrvzibvfGenJZz/HdVIORSRilAOZX8wQcpuOcR9DMHeHEnN/HBz2+eEEzqTK6l1EudMHRxMfn6qqqtWlf//WmqkgYHiem0F35mgcli4sPW5fkUnr+/Pb8EU8T2/+MWdl3GqqknKwUZIR1DFSNvgNcsena0avT0YniEpjPfmzRNHNPuhhy++uDO5pk+P3xcXUn3LFthrr+Tnd+SRnclldMb27fDgg/nnO3WqG30fFTb7vvtan79+vRvhPDiYjzxz5rjfIkYxP/xw/nmGEY0rGbqIxYsX65o1a3LLb9GicudZABc+Y/v2xgsVnHehWxgbc7/LlqWLa5OGGTPiP/gddohWWiITFV5w3S+YFixwhcCGDfnIaVTPrFnw5JON9QcecP/z/Pnu+0qjIPJkaAgef7yY8uTFL4aHHuo8HxG5UVUXR+2zlkMEhcYriWHbNleArV/fnYoBnFI49dT8FAO4vE49tXkSFIhvzYTrO8H1YAtt5507m5zFqBdPPTXxPbn8crf9oYfKVwzQ+I5bBZtshxUr8s0vkjh7UzelvH0OVYeysJScfLvy4GB7537xi255/vzonlp1GqhmqXtTcJxO3uFKbrghn7IO8zlkY3TUTcNn1BM/ausrXtG8r5WvZuNGOOusxrpq8zFR2wwjK6qu1Qvd2UI15RDByIhzpk6dWrUkRhzr1sHNN0/cNmkSvPnNMHly8rlBZ6gf7jvI8HDn8hndRxGdQNav795Q8qYcYhgZgZkzq5aiv9ltt+R5H8LO5G3b4JprWk98M39+Y/mKK5r3W8uxPymqxdhqQp68/RF5YcohgSocw0NDMG1a+detI7/+teuymuXj2by59Uf+wAPu96mn4IwzmveffLKZloz8WLfOOcnjuPDC7Hn++MfRnTTyxJRDzXj8cTjuuKqlqAeqjZnjiqhdbdwIL7zQvH3DBti6tb0885rRz+gt8q5sjI46paPqftudgS4JUw41o1vtk0Xw3e+635GR/ObGLpLBQfeRGkYW2qkMhrtxFzGftA2CS6DMUcrh6/bA39Ix/vOfM8cNbopyHteJgw6C667Ld5yHYaRFJPs3YoPg2iTJGVokphgcfq/u9evrrxgArrrKFINRHQsX5pufKYcEVq0yG7KRnla9pAyjKGbMcH6IPDHlkMDIiIvXYhiGUVcWLIiPP9YJLYYLGU88UbUERlWY78foBm66CebNyz9fazm0IG87ntE9qNooeaP+FOWPM+XQgiVLqpbAqIrBQbjooqqlMIxkTDlUxOrVVUtgVIXN9WB0A5ddVky+phxaUMXcDkZ9OOaYqiUwjGT+4R8sfEYl+DOzGYZh1JFt2yx8hmEYhhFBEeEzTDm0wLqyGobRDeRtAjfl0ALrymoYRjdg4TNKZnQ0ORa7YRhGHdhrr3zzs2KvBSMjMHt21VIYhmEkc9VV+eZnyiEF5ncwDKPu5B340ZRDCszvYBhGv2HKIQV5h8I1DMOoO6YcUpB3KFzDMIy6Y8rBMAzDaMKUQ0p23LFqCQzDMJLJM4SGKYeUfOlLVUtgGIaRTJ4hNEw5pMT8DoZh1J08Q2iYcjAMw+gR8ux2b8ohA0NDVUtgGIYRT54zV5pyyMCqVVVLYBiGEU+eM1eWrhxE5BARuUNE1orI6RH7p4nIt7z9vxaRRWXLGMfIiAXhMwyjvqxbl19epRZ1IjIJOA84FNgHeK+I7BM67CTgSVXdC/g8cFaZMrbi5JOrlsAwDKN4yq4HvxpYq6r3qOpm4JvA4aFjDgcu8ZYvAw4SESlRxkTOPx+mTq1aCsMwjGIpWznsBtwXWL/f2xZ5jKpuBZ4GmlzBIrJMRNaIyJrHHnusIHGjuegimDKlebuvwiZNKlUcwzCM3ClbOUS1ALSNY1DVC1V1saounjdvXi7CpWVkBC6+GIaHnUIYHoaxMdi+HVThkkuilYdhGEaR5OkTnZxfVqm4H9g9sL4AeDDmmPtFZDKwM1C7GRVGRuIHxvnbTz4ZNmwoTybDMPqbPH2iZbccbgD2FpE9RGQqcDRwReiYK4Cl3vK7gCtVtanlUHdGRuC551yLItzCUG2k8P7ly2FwMP11Bgfd+Itg/mNjNibDMPqN5cudTzQvpOxyV0SWAF8AJgEXqeqoiHwCWKOqV4jIDsDXgH1xLYajVfWepDwXL16sa9asKVp0wzCMnkJEblTVxVH7yjYroaqrgdWhbWcEll8AjipbLsMwDKOBDekyDMMwmjDlYBiGYTRhysEwDMNowpSDYRiG0UTpvZWKQEQeA9oNOTUXeDxHccrC5C4Xk7s8ulFm6E65h1U1chRxTyiHThCRNXFdueqMyV0uJnd5dKPM0L1yx2FmJcMwDKMJUw6GYRhGE6Yc4MKqBWgTk7tcTO7y6EaZoXvljqTvfQ6GYRhGM9ZyMAzDMJow5WAYhmE00dfKQUQOEZE7RGStiJxetTw+IrK7iPxSRH4vIr8TkVO97WeKyAMicrOXlgTO+ah3H3eIyFsrlP1eEfmtJ98ab9scEfmZiNzl/c72touIfNGT+1YR2a8imV8aeKY3i8gzInJaHZ+3iFwkIo+KyG2BbZmfr4gs9Y6/S0SWRl2rBLn/j4j8wZPteyIyy9u+SESeDzz3LwXOeaX3fq317q3QKYRj5M78XtS1rElEVfsy4UKG3w3sCUwFbgH2qVouT7Zdgf285ZnAncA+wJnAP0Ycv48n/zRgD+++JlUk+73A3NC2zwKne8unA2d5y0uAH+Nm/9sf+HUNnv0k4GFguI7PGzgQ2A+4rd3nC8wB7vF+Z3vLsyuQ+y3AZG/5rIDci4LHhfL5b+C13j39GDi0ArkzvRd1LmuSUj+3HF4NrFXVe1R1M/BN4PCKZQJAVR9S1Zu85WeB39M813aQw4FvquomVf0jsBZ3f3XhcOASb/kS4IjA9kvVcT0wS0R2rULAAAcBd6tq0oj7yp63ql5N88yIWZ/vW4GfqeoTqvok8DPgkLLlVtWfqpsnHuB63MyQsXiy76Sq16krjS+lca+FEPO844h7L2pb1iTRz8phN+C+wPr9JBfAlSAii3ATH/3a2/QBrxl+kW8+oF73osBPReRGEVnmbXuRqj4ETvEBu3jb6yS3z9HANwLrdX/ekP351k1+gBNxLQGfPUTkNyLyXyLyP7xtu+Fk9alS7izvRR2fd0v6WTlE2Spr1a9XRHYEvgucpqrPABcAfwa8AngI+Jx/aMTpVd3L61V1P+BQ4O9F5MCEY+skN+Kmrj0M+I63qRuedxJxctZKfhFZCWwFxr1NDwELVXVf4EPA10VkJ+ojd9b3oi5yZ6KflcP9wO6B9QXAgxXJ0oSITMEphnFVvRxAVR9R1W2quh34Mg1TRm3uRVUf9H4fBb6Hk/ER31zk/T7qHV4buT0OBW5S1UegO563R9bnWxv5PWf43wIjnqkIzyyz3lu+EWevfwlO7qDpqRK523gvavO8s9DPyuEGYG8R2cOrMR4NXFGxTIDrZQL8O/B7VT07sD1oj38H4PeguAI4WkSmicgewN44x12piMigiMz0l3EOx9s8+fweMUuBH3jLVwDHeb1q9gee9s0jFfFeAialuj/vAFmf70+At4jIbM8k8hZvW6mIyCHA/wYOU9WNge3zRGSSt7wn7vne48n+rIjs730jx9G41zLlzvpe1LasSaRqj3iVCdeb405czWRl1fIE5DoA1+y8FbjZS0uArwG/9bZfAewaOGeldx93UHAPjgS598T1xLgF+J3/TIEh4BfAXd7vHG+7AOd5cv8WWFzhM58BrAd2Dmyr3fPGKa+HgC24GulJ7TxfnI1/rZdOqEjutThbvP+Of8k79p3e+3MLcBPw9kA+i3GF8d3AuXhRHkqWO/N7UdeyJilZ+AzDMAyjiX42KxmGYRgxmHIwDMMwmjDlYBiGYTRhysEwDMNowpSDYRiG0YQpB6On8SJoqrc8y1uvJPqrJ8MrPBnmROxTETmzArEMowlTDkav8xVcFE+AWcDHcVE2q+IVngxNygEn51fKFccwoplctQCGUSSqej8Tg7XlijdSd4q6aJsdoS5yqmHUAms5GD2Nb1byotv+0dv8ZW+bisjxgWOPFJHrRWSjiDwlIt8RkYWh/O4VkTEROVFE/gBsBt7m7ftnEblJRJ4WkcdF5EovbIV/7vHAxd7qXQEZFnn7m8xK3iQx14mb/OZpEfm+iLw0dMxVIvIrETnYu/5GEblNRAoNZ230NqYcjH7hIeBIb/nTOBPOa4EfAYjIKbhAh7cD7wJOBv4C+C8/XlSAN+Gihf4zbh6EW73tuwGfx80xcDwuAN7VIvJX3v4fAZ/0lo8KyBAZT8qLPfQj4DngPcByT6ZfiUg45POfAauAs737fAi4TET2SnwqhhGDmZWMvkBVN4nIb7zVe4ImHC80+lnAxap6YmD7r3HxcE4CvhDIbjbwSlV9OHSNvwucOwn4T1yMoJOAU1X1MRG52zvkZlVd20LsT+JmaTtUvUlxROQ6T6YP4xSUz1zgQFW9yzvuJpyCeDfwqRbXMYwmrOVgGK72vhMwLiKT/YTzVfwBN1VkkOvDigHAM+v8UkTW4+Yn2IILNf3S8LGt8KLa7gd8SxuzpaFuhrFrgTeETrnLVwzecY/iWi4LMYw2sJaDYTRmTvt5zP4nQ+tNZiCve+xqXOjrk7xjtuF6H+3QhkyzcVFVo0xO/hzXQaKmstzU5rUNw5SDYeBCdYPzE/wuYv+zofWoUMbvxLUWjlTVLf5Gb76Ep9qQ6UnvOi+O2PdiGjIbRiGYcjD6iU3e7/TQ9v+LUwB7qeolbeY9A9dS+P+KQ0TejDPr/DFwXJwME1DVDSJyI3CUiJypqtu8PIeB1wHntCmnYaTClIPRTzyCq3EfLSK3AhuAP6rqehH5CHCeiMzDTXT/NK730RuAq1T16y3y/k/gNOCrInIxztfwMeCB0HG3e79/LyKX4PwSt8aMk/gYrrfSD0XkfGBHXA+pp2nMW2wYhWAOaaNvUDfn79/h7Pk/x03f+HZv378Bh+Gcx1/DKYh/xlWgbk6R90+ADwKvB36Im2ntONxsZ8HjbgHO9K77K0+G+TF5/iduDMUs4NvAl4DfAweoN1e3YRSFzQRnGIZhNGEtB8MwDKMJUw6GYRhGE6YcDMMwjCZMORiGYRhNmHIwDMMwmjDlYBiGYTRhysEwDMNowpSDYRiG0cT/A+rULOfmxrBuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from DeepPurpose import oneliner\n",
    "from DeepPurpose.dataset import *\n",
    "\n",
    "FILE_PATH = load_AID1706_txt_file()\n",
    "\n",
    "oneliner.repurpose(*load_SARS_CoV_Protease_3CL(), *load_antiviral_drugs(no_cid = True),  *read_file_training_dataset_bioassay(FILE_PATH), \\\n",
    "    split='HTS', convert_y = False, frac=[0.8,0.1,0.1], finetune_batch_size = 128, pretrained = False, agg = 'agg_mean_max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
